Specialization: Transport Engineering and Logistics Report ...

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Specialization: Transport Engineering and Logistics Report number: 2019.TEL.8323 Title: Improvement of road traffic

sustainability by implementation of priority weights for trucks in predictive signalized intersection control

Author: L. Haanstra

Title (in Dutch) Verbetering van de duurzaamheid van het wegverkeer door de invoering van

prioriteitsgewichten voor vrachtwagens in voorspellende verkeersregelingen op kruispunten

Assignment: Masters thesis

Confidential: Yes (until May 31, 2019)

Chair graduation committee (university): Dr. ir. H. Polinder

Supervisor (university): Dr. ir. X. Jiang

Supervisor (company): ing. A.P. Verhoeven (Siemens Mobility B.V.,

Zoetermeer)

Date: March, 2019

http://www.mtt.tudelft.nl/

Leroy Haanstra

Improvement of road traffic sustainability by implementation of priority weights for trucks in predictive signalized intersection control

Improvement of road traffic sustainability by implementation of priority weights for trucks in predictive signalized intersection

control

By

Leroy Haanstra

in partial fulfilment of the requirements for the degree of

Master of Science

in Mechanical Engineering

at the Delft University of Technology, to be defended publicly on Wednesday March 20, 2019 at 1:00 PM.

Student number: 4248007

Report number: 2019.TEL.8323 Thesis committee: Dr. ir. H. Polinder TU Delft

Dr. ir. X. Jiang TU Deflt Dr. ir. H. Taale TU Delft

ing. A.P. Verhoeven Siemens

This thesis is confidential and cannot be made public until May 31, 2019.

iii

Abstract In the European Union Road freight transport volume is expected to grow 78% between 2000 and

2030, which results in more trucks on the road network. The worldwide estimated trend shows an

increase of 150 million freight vehicles and an increase of 240 million passenger vehicles. The growth

of both vehicle classes will have a major impact on the road network and the roads will become

congested. Especially in dense urban environments with many intersections. Further, trucks have a

detrimental impact on traffic flows, especially at intersections, because of their slow dynamics and large

size. In addition, a stopping truck results in higher emissions and fuel consumption compared to a car.

However, today’s traffic controllers are not capable of optimizing traffic flow at intersections based on

classification of different vehicles. Therefore, it would be beneficial to all vehicles involved if the number

of stops for trucks would be reduced to a minimum, by servicing each vehicle class in a different way.

Throughout this research the focus is to develop a signalized intersection controller which is able to

reduce the number of stops for trucks, while maintaining an equal level of service for the other modes

of transport. Extensive literature studies provided important insights into the development of signalized

intersection controllers. A selection of the techniques found in the literature is used to develop a new

truck signal priority controller design. The basic idea is to use a weighted traffic light schedule in

combination with priority weights to enable truck signal priority. The design is evaluated in a case study

and simulated for multiple configurations. This leads to an overview of the performance in terms of the

number of stops and vehicle delay.

From the results several conclusions can be drawn on the optimal weight configuration, which is

compared to a state-of-the-art model predictive intersection controller. The results showed an reduction

of the total number of stops by 751 and total vehicle delay by 304 minutes for trucks over a week

(26,6% and 20,4% respectively). While, the total number of stops and total vehicle delay for cars

increased, by 155 stops and 201 minutes (0,1% and 0,3% respectively). However, the overall total

number of stops and total vehicle delay were reduced by 596 stops and 103 minutes (0,42% and 0,16%

respectively). It can be concluded that the proposed truck signal priority controller design can reduce

the number of stops for trucks at a signalized intersection, while maintaining the overall traffic flow at

least as good as a state-of-the-art model predictive intersection controller.

v

Contents Abstract ............................................................................................................................... iii

Contents ............................................................................................................................... v

List of abbreviations ........................................................................................................... vii

List of Figures ...................................................................................................................... ix

List of Tables ....................................................................................................................... xi

1. Introduction .................................................................................................................. 1

1.1. Problem statement ......................................................................................................... 1

1.2. Research objective ......................................................................................................... 3

1.3. Research question .......................................................................................................... 3

1.4. Research scope .............................................................................................................. 3

1.5. Approach ....................................................................................................................... 4

1.6. Report structure ............................................................................................................. 5

2. Literature review ........................................................................................................... 7

2.1. Signalized intersection controllers .................................................................................... 7

2.1.1. Basics..................................................................................................................... 8

2.1.2. Fixed-time control ................................................................................................... 9

2.1.3. Traffic-actuated control ........................................................................................... 9

2.1.4. Traffic-adaptive control ......................................................................................... 10

2.1.5. Model Predictive Control ........................................................................................ 10

2.2. Vehicle detector data approach ..................................................................................... 11

2.2.1. Inductive-loop detector ......................................................................................... 12

2.2.2. Magnetic detectors ................................................................................................ 12

2.2.3. Microwave radar detector ...................................................................................... 13

2.2.4. Infrared detector .................................................................................................. 14

2.2.5. Ultrasonic detector ................................................................................................ 15

2.2.6. Acoustic detector .................................................................................................. 15

2.2.7. Video Image Processor .......................................................................................... 15

2.2.8. Floating Car Data .................................................................................................. 15

2.3. Priority strategy............................................................................................................ 18

2.3.1. Emergency Vehicle Signal Pre-emption ................................................................... 19

2.3.2. Transit Signal Priority ............................................................................................ 19

2.3.3. Truck Signal Priority .............................................................................................. 21

2.4. Conclusions .................................................................................................................. 22

3. DIRECTOR ................................................................................................................... 25

3.1. Cumulative travel time delay ......................................................................................... 26

3.2. Uniform distribution of arrivals ...................................................................................... 26

3.3. Schedule decision ......................................................................................................... 27

vi Contents

3.4. Fixed schedule ahead of time ........................................................................................ 28

3.5. Conclusions .................................................................................................................. 28

4. Truck Signal Priority controller design ....................................................................... 29

4.1. Truck arrivals ............................................................................................................... 29

4.2. Priority weights ............................................................................................................ 30

4.3. Truck weights .............................................................................................................. 32

4.4. Schedule decision ......................................................................................................... 33

4.5. Conclusions .................................................................................................................. 33

5. Experimental setup ..................................................................................................... 35

5.1. Case study ................................................................................................................... 36

5.2. Simulation tools ........................................................................................................... 36

5.2.1. PTV Vissim ........................................................................................................... 37

5.2.2. Python ................................................................................................................. 37

5.3. Simulation data ............................................................................................................ 37

5.3.1. Historic data ......................................................................................................... 37

5.3.2. Truck data ............................................................................................................ 38

5.4. Simulation model ......................................................................................................... 38

5.5. Conclusions .................................................................................................................. 40

6. Simulation results ....................................................................................................... 41

6.1. Truck Signal Priority performance .................................................................................. 41

6.2. Priority weights sensitivity analysis ................................................................................ 44

6.3. Robustness check ......................................................................................................... 47

6.4. Emission analysis ......................................................................................................... 49

6.5. Discussion.................................................................................................................... 50

6.5.1. Truck Signal Priority performance .......................................................................... 50

6.5.2. Priority weights sensitivity analysis ......................................................................... 50

6.5.3. Robustness check ................................................................................................. 51

6.5.4. Emission analysis .................................................................................................. 51

7. Conclusions and recommendations ............................................................................ 53

7.1. Conclusions .................................................................................................................. 53

7.2. Recommendations ........................................................................................................ 54

References ......................................................................................................................... 57

A. Scientific paper ........................................................................................................... 63

B. Python modules and packages .................................................................................... 73

C. Vehicle arrivals ............................................................................................................ 75

D. Simulation results ....................................................................................................... 79

E. Simulation data ........................................................................................................... 85

F. Emission data ............................................................................................................ 103

vii

List of abbreviations ADAS Advanced Driver Assistance Systems

ADVANCE Advanced Driver and Vehicle Advisory Navigation Concept

BPTT Backpropagation Through Time

C-ITS Cooperative Intelligent Transport Systems

CO Carbon monoxide

CO2 Carbon dioxide

COM Component Object Model

DIRECTOR Data-driven Intersection and Road Environment Controller for Traffic Optimization in Real-time

DoE Design of Experiments

EVSP Emergency Vehicle Signal Pre-emption

FCD Floating Car Data

GLOSA Green Light Optimal Speed Advice

GPS Global Positioning System

I2V Infrastructure-to-Vehicle

ITS-G5 IEEE 802.11p, WIFI-P

KPI Key Performance Indicator

LSTM Long Short-Term Memory

LTE Cellular

MPC Model Predictive Control

NOx Nitrogen oxide

OD Origin-Destination

PG Phase Group

RADAR RAdio Detection And Ranging

RNN Recurrent Neural Network

SOIC Self-Organizing Intersection Controller

TkSP Truck Signal Priority

TSP Transit Signal Priority

V2I Vehicle-to-Infrastructure

V2V Vehicle-to-Vehicle

V2x Vehicle-to-Everything

VIP Video Image Processor

VOC Volatile Organic Compounds

xFCD Extended Floating Car Data

ix

List of Figures Figure 1 - Estimated trend of worldwide number of freight vehicles [8] ............................................ 2

Figure 2 - Flowchart of the approach .............................................................................................. 4

Figure 3 - Report structure ............................................................................................................ 5

Figure 4 - Layout chapter 2: Literature review ................................................................................ 7

Figure 5 - Conflict area [14] ........................................................................................................... 8

Figure 6 – Three phase group examples ......................................................................................... 8

Figure 7 - Magnetic anomaly induced in the Earth’s magnetic field by a magnetic dipole [46] .......... 13

Figure 8 - Perturbation of Earth’s magnetic field by a ferrous metal vehicle [46] ............................. 13

Figure 9 - Microwave radar operation [46] .................................................................................... 14

Figure 10 - Extended FCD examples [55] ...................................................................................... 16

Figure 11 - Comparison between IEEE 802.11p and cellular connectivity pipes to the car [57] ......... 16

Figure 12 - 5G the development compared to 4G [59] ................................................................... 17

Figure 13 - Layout chapter 3: DIRECTOR ...................................................................................... 25

Figure 14 - Arrival of vehicles over 10 seconds [93] ...................................................................... 26

Figure 15 - Transition from ρ(t) to ρ[T] [93] ................................................................................. 27

Figure 16 – Illustration of the switching penalty [93] ..................................................................... 28

Figure 17 - Layout chapter 4: Truck Signal Priority controller design .............................................. 29

Figure 18 - Point of detection ...................................................................................................... 30

Figure 19 - Truck arrivals per time bin .......................................................................................... 30

Figure 20 - Layout chapter 5: Experimental setup ......................................................................... 35

Figure 21 - Schematic overview of the case study intersection. ...................................................... 36

Figure 22 - Truck detections (marked with red dots) ..................................................................... 38

Figure 23 - PTV Vissim network layout of the intersection .............................................................. 39

Figure 24 – Layout chapter 6: Simulation results ........................................................................... 41

Figure 25 - Comparison: All ......................................................................................................... 43

Figure 26 - Comparison: Cars ...................................................................................................... 43

Figure 27 - Comparison: Trucks ................................................................................................... 43

Figure 28 – Sensitivity analysis: 24 hours (1/2) ............................................................................. 44

Figure 29 – Sensitivity analysis: 24 hours (2/2) ............................................................................. 45

Figure 30 - Sensitivity analysis comparison: All ............................................................................. 46

Figure 31 - Sensitivity analysis comparison: Cars .......................................................................... 46

Figure 32 - Sensitivity analysis comparison: Trucks ....................................................................... 46

Figure 33 – Sensitivity analysis: Week (1/2).................................................................................. 47

Figure 34 – Sensitivity analysis: Week (2/2).................................................................................. 48

Figure 35 - Carbon monoxide emissions ....................................................................................... 49

Figure 36 – Nitrogen oxide emissions ........................................................................................... 49

Figure 37 - Volatile Organic Compounds emissions ........................................................................ 49

x List of Figures

Figure 38 - Fuel consumption ...................................................................................................... 49

Figure 39 – Week ........................................................................................................................ 80

Figure 40 – Tuesday ................................................................................................................... 80

Figure 41 – Wednesday ............................................................................................................... 81

Figure 42 - Thursday ................................................................................................................... 81

Figure 43 - Friday ....................................................................................................................... 82

Figure 44 - Saturday ................................................................................................................... 82

Figure 45 - Sunday ...................................................................................................................... 83

Figure 46 - Monday ..................................................................................................................... 83

xi

List of Tables Table 1 - Growth in global freight transport [6] ............................................................................... 2

Table 2 - Detector types .............................................................................................................. 12

Table 3 - Traffic signal control treatments for different traffic modes [64] ...................................... 18

Table 4 - Key characteristic of different transit signal priority controls [76] ..................................... 21

Table 5 - Priority weight index to corresponding time bin .............................................................. 31

Table 6 - Priority weights example 4.3.1. ...................................................................................... 32

Table 7 - Truck arrivals example 4.3.1. ......................................................................................... 32

Table 8 - Results example 4.3.1. .................................................................................................. 32

Table 9 - Number of vehicles: 24 hours ........................................................................................ 42

Table 10 – Total stops ................................................................................................................. 42

Table 11 – Total vehicle delay ...................................................................................................... 42

Table 12 – Priority weight data set ............................................................................................... 44

Table 13 – Total stops: Sensitivity analysis ................................................................................... 45

Table 14 - Total vehicle delay: Sensitivity analysis ......................................................................... 45

Table 15 - Number of vehicles: Week ........................................................................................... 48

Table 16 – Total stops: Week ...................................................................................................... 48

Table 17 - Total vehicle delay: Week ............................................................................................ 48

Table 18 - Car arrivals ................................................................................................................. 76

Table 19 - Truck arrivals .............................................................................................................. 77

Table 20 - Simulation data: Week number of stops ....................................................................... 86

Table 21 - Simulation data: Week vehicle delay (s) ....................................................................... 87

Table 22 - Simulation data: Tuesday number of stops ................................................................... 88

Table 23 - Simulation data: Tuesday vehicle delay (s) ................................................................... 89

Table 24 - Simulation data: Wednesday number of stops .............................................................. 90

Table 25 - Simulation data: Wednesday vehicle delay (s) .............................................................. 91

Table 26 - Simulation data: Thursday number of stops .................................................................. 92

Table 27 - Simulation data: Thursday vehicle delay (s) .................................................................. 93

Table 28 - Simulation data: Friday number of stops ...................................................................... 94

Table 29 - Simulation data: Friday vehicle delay (s) ...................................................................... 95

Table 30 - Simulation data: Saturday number of stops .................................................................. 96

Table 31 - Simulation data: Saturday vehicle delay (s) .................................................................. 97

Table 32 - Simulation data: Sunday number of stops ..................................................................... 98

Table 33 - Simulation data: Sunday vehicle delay (s) ..................................................................... 99

Table 34 - Simulation data: Monday number of stops .................................................................. 100

Table 35 - Simulation data: Monday vehicle delay (s) .................................................................. 101

Table 36 - Emission data ........................................................................................................... 104

1

1 1. Introduction

Signalized intersections play an important role in modern society. The introduction of signalized

intersection controllers gave structure and a way of automatic traffic handling at intersections. Only

with the economic growth that developed countries faced an exponentially increasing demand for

personal mobility occurred [1]. It quickly resulted in congestions at signalized intersections in urban

environments. Congestion involves queuing, lower speeds, and increased travel times, which impose

costs on the economy and generate multiple impacts on urban regions and their inhabitants [2]. To

eliminate the congestion, signalized intersections could be replaced with bridges, tunnels or non-

signalized roundabouts. This option is, however, in many cases, both economically and spatially, not

feasible [1]. One alternative option to improve efficiency of urban intersections would be the innovation

of the on-street traffic controllers. However, traffic inefficiencies will still occur, because of the

disruption of traffic flow caused by a red light, even with the latest innovations of traffic controllers. In

addition, a reaction to an unanticipated switch from green to amber causes safety concerns, as drivers

may suddenly stop or quickly accelerate. Apart from the disruption of traffic flow and safety concerns,

the acceleration and deceleration behaviour cause the largest amount of fuel consumption and CO2

emissions [3]. These stops result in over 50% of the fuel consumption of a vehicles trip [4]. Moreover,

one stop of a vehicle could create a backward moving shockwave that induces a cyclic driving state of

acceleration and idling. This behaviour is responsible for up to two thirds of the total energy loss at

intersections [3], [4]. Additionally, once the traffic light turns green, the inability for drivers to anticipate

when they should accelerate from stop, and the time it takes to accelerate to free-flow speed, results

in a queue discharge rate that can be as low as 75% of the road’s capacity [5]. It is therefore the goal

to reduce the number of stops for vehicles in future signalized intersection controllers.

1.1. Problem statement

Worldwide freight transport volumes are increasing and the strongest expected growth is in road freight

transport, as shown in Table 1 [6]. Road freight transport volume in the European Union is expected to

grow 78% between 2000 and 2030 [7]. This will pair with an increasing volume of trucks driving on

the road, displayed in Figure 1 [8].

2 1. Introduction

Table 1 - Growth in global freight transport [6]

Figure 1 - Estimated trend of worldwide number of freight vehicles [8]

Aside from the increase in truck, the number of passenger vehicles is also expected to increase [9].

The growth of both vehicle classes will have a major impact on the road network and the roads will

become congested. Especially in dense urban environments with many intersections. Further, trucks

have a detrimental impact on traffic flows, especially at intersections, because of their slow dynamics

and large size [10]. The time for a heavy truck to respond to a traffic light, accelerate and cross the

intersection is much higher than that of normal passenger cars [11]. In addition, a stopping truck also

results in much higher emissions and fuel consumption. Despite the different vehicle characteristics

between trucks and passenger cars, current traffic controllers do not service them differently. Instead,

the traffic controllers will service both as equal. Hence, the problem could be described as:

“Today’s traffic controllers are not capable of optimizing traffic flow at intersections based on

classification of different vehicles.”

Therefore, it would be beneficial to all vehicles involved if the number of stops for trucks would be

reduced to a minimum, by servicing each vehicle class in a different way. For example, the cars stopping

behind a truck at a red light would be limited to the lower acceleration of the truck driving in front when

the light turns green. This would increase the total time for clearing the queue and less vehicles could

be serviced during green time. Next to the traffic delay due to slow dynamics and less vehicles serviced

during green, reducing the number of stops for truck has also other benefits. These benefits are related

to the environmental impact of a signalized intersection. The high fuel consumption and high emissions

resulting from a stop and go for a truck would be reduced. But also, the high pavement wear and

particulate matter emissions due to the braking of a heavy truck would be minimized. A possible solution

is the implementation of priority weights for trucks approaching the intersection. By making a distinction

1.2. Research objective 3

between vehicles classes and weighing trucks higher than cars would reduce the number of stops for

trucks, as they get more priority for green light.

1.2. Research objective

The goal of this research is to reduce the number of stops for trucks at signalized intersections by

granting priority to trucks via implementation of priority weights for different vehicle classes in

DIRECTOR (Data-driven Intersection and Road Environment Controller for Traffic Optimization in Real-

time). This research focusses on two important Key Performance Indicators (KPI’s): the traffic flow and

the number of stops at signalised intersections for trucks. In this research the traffic flow is indicated

with the vehicle delay. The implementation of priority weights for different vehicle classes provides a

possible improvement to the traffic flow and/or reduction the number of stops for trucks. It is coherent

that this can also reduce the fuel consumption, CO2 emission and safety concerns. Accordingly, finding

the means with which to develop and evaluate the implementation is the research objective.

1.3. Research question

The main research objective is to investigate how the implementation of priority weights, in a model

predictive control-based signalized intersection controller, could lead to an reduction in the number of

stops for trucks. However, the implementation of priority weights should not decrease the traffic flow

of other vehicles. Therefore, the main research question is stated as:

“Can a model predictive control-based signalized intersection controller with priority weights lead to

a reduction in the number of stops for trucks at a signalized intersection, while maintaining

traffic flow at least as good as a state-of-the-art model predictive controller?’’

In order to answer the main question, several sub questions need to be answered:

1. What are the state-of-the-art signalized intersection control techniques?

2. What are the basic principles of the state-of-the-art model predictive controller?

3. How should the priority weights be assigned to different vehicle types?

4. How could the intersection controller with priority weights be evaluated?

5. Does a change in priority weights affect the performance in terms of traffic flow?

6. Can a model predictive controller with priority weights improve the performance of a state-of-

the-art model predictive controller in terms of reducing the number of stops?

7. How does the controller perform at different traffic demands?

1.4. Research scope

According to the research objectives, the research is bounded by a scope. This is used to set limitations

which are required to ensure that realistic solutions are found and to narrow the size of the research.

Within the search for an optimal state of operation, the practical restrictions of legal and road safety

boundary conditions must be considered. As well as, industry standards should be used to meet the

requirements of a practical application. The possible inclusion of other data sources, such as Floating

4 1. Introduction

Car Data (FCD) should be explored. However, the means of receiving the floating car data form the

road users is not part of the study and it is assumed that all floating car data from trucks is available

for use. Furthermore, the intersection geometry is simplified and the impact of pedestrians, bicycles,

public transport vehicles and emergency vehicles (police, ambulance and fire trucks) is not investigated,

although the traffic light controller should be able to deal with these types of road users.

1.5. Approach

The research approach is visualized in a flowchart, displayed in Figure 2. The first step will be to study

the current model available. This step is very important as it will form the baseline of the proceeding

work. A literature study will be performed to examine the related work and to determine the

requirements. Based on the requirements a method to implement the priority weight will be proposed.

The next step is to define the proposed method in a mathematical form. Following the mathematical

form, a design of the control algorithm is made. Afterwards, the designed control algorithm should be

implemented in the current model. The new model should now be evaluated based on the key

performance indicators of section 1.2. Evaluation in a real environment results in high implementation

and start-up costs. Also, there are safety concerns to be considered. Therefore, a simulation

environment will be used as reality. This means the intelligent traffic controller will be evaluated within

the simulation environment and not on a physical intersection. The simulation environment used in this

study is PTV Vissim, an advanced simulation and visualisation tool. Based on the results of the

evaluation, the model should be adjusted to answer the research questions and enhance the

performance of the model.

Figure 2 - Flowchart of the approach

1.6. Report structure 5

1.6. Report structure

Based on the approach discussed in the previous section, this section describes the structure of this

research. The report starts with an overview of the state-of-the-art signalized intersection control

techniques and a selection of the most promising methods and techniques (chapter 2). Thereafter, the

basic principles in the design of the model predictive controller DIRECTOR are described (chapter 3),

which are required as preliminary knowledge in this thesis. Subsequently, the development of the

design of the Truck Signal Priority controller elaborated (chapter 4). Next, the experimental setup and

a case study, used to evaluate the performance of the proposed truck signal priority controller design,

are defined (chapter 5). Afterwards, the results of the evaluation of the case study are presented and

discussed (chapter 6). Finally, the report is concluded and recommendations for future work are

presented (chapter 7). The relationship between the different chapters is shown in Figure 3.

Figure 3 - Report structure

7

2 2. Literature review

This chapter presents a literature review of relevant topics to this research. The goal of this chapter is

to explore the state-of-the-art signalized intersection control techniques. Starting with the basics of

signalized intersection controllers and continues to review different control methods (Section 2.1).

Afterwards, different types of vehicle detectors are presented (section 2.2). Thereafter, the priority

approaches of signalized intersections are explored (section 2.3). Finally, the literature review is

concludes with a selection of the most promising methods and techniques (section 2.4). The relationship

between the different sections is shown in Figure 4.

Figure 4 - Layout chapter 2: Literature review

This chapter will answer the first sub question, presented in section 1.3, with the following questions:

(1) What are the state-of-the-art signalized intersection control techniques?

• What is the state-state-of-the-art signalized intersection controller?

• How could the intersection controller identify different vehicle type?

• Which priority strategy could be applied for trucks?

2.1. Signalized intersection controllers

Traffic control on an intersection can be summed up as a mechanism for permitting and disallowing the

right of way for different traffic streams with sequences of traffic light indications [12]. Traffic signals

prevent accidents and influence the throughput of intersections [13]. The disallowance of traffic streams

in a direction results in a negative impact on local traffic flow. It is therefore the challenge to design a

signalized intersection controller with the least negative impact on the traffic flow. This section starts

with the basics of signalized intersection controllers. Afterwards, four main types of signalized

intersection control are discussed:

1. Fixed-time control

2. Traffic-actuated control

3. Traffic-adaptive control

4. Model predictive control


2.1.1. Basics

The basics of signalized intersection controllers are discussed, to create a better understanding of the

terminology used in this research. An intersection consists of a number of approaches and a crossing

area. The crossing area connects the approaches to the intersection. Each approach may have one or

more lanes, but has a unique and independent queue. An approach could also be defined as a link. The

intersection provides safe access through the crossing area from an origin link to a destination link. A

connected origin link and destination link are called an Origin-Destination pair (OD). Each Origin-

Destination has one or more lanes and each lane has its own signal. The purpose of a signal is to grant

or prohibit access to the crossing area via the different signal lights, respectively green and red. The

term for granting access is servicing. A signal group is a combination of signals which are always

serviced together and have the same Origin-Destination. Multiple signal groups could be serviced

together as well. However, one must be aware of possible conflict areas. A conflict area is the place

where two Origin-Destination paths cross each other, as shown in Figure 5. A combination of signal

groups which can be serviced simultaneously is defined as a Phase Group (PG), multiple examples are

displayed in Figure 6. The phase is the period a Phase Group is serviced, i.e. the green phase. The

sequence of phases is defined as the schedule of the intersection controller. The goal is to optimize the

schedule according to the desired optimization criteria. Originally the most used criteria are the

cumulative or average travel time delay. Other criteria that are often used are the number of stops or

the queue lengths at an intersection. Today, the importance of reducing the environmental impact

shifted the focus of the optimization criteria to fuel efficiency and emissions. In practice a combination

of criteria is often used, resulting in a trade-off at the scheduling decision.

Figure 5 - Conflict area [14]

Figure 6 – Three phase group examples

2.1. Signalized intersection controllers 9

2.1.2. Fixed-time control

The most straightforward design of a traffic light controller is fixed-time control. In a fixed-time control

strategy is the duration of each phase determined offline and switched in a pre-set cycle. This approach

assumes that the traffic patterns can be predicted accurately based on historical data [14]. The fixed-

time coordinated strategies are applicable to under-saturated traffic conditions [15]. As fixed-time

controllers can operate without traffic detectors installed at the intersection, the construction cost is

much lower than with traffic actuated and traffic-adaptive control [14].

The fixed-time controllers most found in literature are MAXBAND [16], [17], MULTIBAND [18], [19] and

TRANSYT [20]. The first, MAXBAND, solves a binary-mixed-integer-linear-programming problem using

the branch and bound method to reach an optimal schedule. The second, MULTIBAND, is an extended

version of MAXBAND. A variety of new aspects are incorporated such as: time clearance of existing

queues, left-turn movements, and multiple signal groups. The third, TRANSYT, is the most famous

fixed-time and is widely used in the United States. TRANSYT is based on a traffic model that is used to

estimate the queue sizes with time. The objective function is to minimize the sum of average queues,

based on the historical data of the network. To solve the objective function a heuristic hill-climb

optimization algorithm is used. This algorithm attempts to find a better solution by introducing an

incremental change to the decision variables and repeats the process until a local minimum is found.

The main drawback of fixed-time control is that it is not able to adapt itself as it is based on historical

rather than on real-time data. [14]. The traffic demand is unpredictable and changes over time [21],

[22]. As a result, the performance decreases over time, since the optimized settings do not match the

current traffic demand.

2.1.3. Traffic-actuated control

Traffic-actuated controllers schedule the next signal phase based on detector information instead of

historical data. Detectors provide the required information in order to respond to the real-time traffic

situation. The detectors that are used most frequently are inductive loop detectors [14]. The controller

could adjust the planned schedule, according to the real-time traffic demand, to seconds before

switching signal phase. The green phase remains, until traffic is detected on a direction outside the

phase group. Additionally, most traffic-actuated controllers have the ability to extend the green time to

ensure that the green phase is terminated comfortably and safely [14]. However, to prevent infinite

green time extension, controllers have a set of pre-defined static parameters such as the extension

time, minimum green time and maximum green time [23], [24]. The main benefit of the controller is

the reduction in wasted green time on empty lane and a faster response on traffic demand. In summary,

the traffic-actuated controllers are more efficient, but also have higher cost compared to a fixed-time

controller [25].


2.1.4. Traffic-adaptive control

Adaptive traffic control systems continuously sense and monitor traffic conditions and adjust the timing

of traffic signals accordingly [14]. The monitoring of the traffic is performed in the same way as the

actuated controller, via detectors. A key difference between traffic-actuated and traffic-adaptive control,

is that a traffic-actuated controller has no foresight on incoming vehicle flows. The traffic-adaptive

controller adds an upstream detector loop to count incoming vehicles on a direction, this creates a

foresight on incoming vehicle and green time durations can be optimized accordingly [26]. Additionally,

a traffic-adaptive controller decision, to either continue the current green phase or to switch to a

different phase, is based on the entire intersection [14]. Whereas the decision at a traffic-actuated

controller is solely based on the presence of demand on the active green phase. A drawback to this

method is that if one or more loop detectors are not operating, the performance of the traffic-adaptive

signal control system might be notably degraded [27].

2.1.5. Model Predictive Control

Model predictive control is in the basics an extended version of the adaptive controller. The key

difference is similar to the difference between actuated and adaptive, the extension of the vision

horizon. The predictive controller has the vision horizon extended to at least the exit flows of the

previous intersection. First implementations of an extended vision horizon are found in SCOOT [28]

and SCATS [29]. These controllers modify the cycles, splits and offsets according to estimations of the

arrivals, based the upstream measurements. More recent developments of predictive controllers are

for example UTOPIA [30], OPAC [31], PRODYN [32], RHODES [33], CRONOS [34] and SURTRAC [35].

These controllers do not explicitly modify the cycles, splits and offsets. Instead, they calculate optimal

switching schedules, based on pre-specified plans for the traffic light phases and the predicted vehicle

arrivals, over a future rolling time horizon [25]. The purpose of using longer horizons for traffic flow

predictions and schedule optimizations is to estimate a full cycle and avoid controller fluctuations due

to short-sighted control outputs [15]. Consequently, the controller must be able to handle the large

uncertainties associated with forecasting traffic flow progression. Three main analytical models for

traffic flow progression are Lighthill & Witham’s fluid dynamic traffic model [36], Pacey’s diffusion

model [37] and Robertson’s platoon dispersion model [20]. Respectively, each model is a modification

of the predecessor. However, these analytical models are limited by a major assumption, which is the

conservation of vehicles from upstream to downstream. As a result, very few locations are suitable for

application. An alternative to the analytical models are data-driven methods. These methods proved

more suitable for traffic flow prediction, as shown by the recurrent neural network (RNN) design

proposed by Helmy [38]. This model returns the arrival flows downstream as an output and uses the

upstream departure flows, the presence of a queue downstream, the traffic split between downstream

lanes, the states of the downstream signals and information about the day of the week and the current

time as inputs. The RNN design is trained with backpropagation through time (BPTT). Helmy evaluated

the model in a case study with real data and outperformed Robertson’s model in terms of prediction

accuracy. However, conventional RNNs trained with BPTT are unable to learn long-term time

2.2. Vehicle detector data approach 11

dependencies [39]. The backwards propagation error accumulates over time, which results in an

unstable network. A solution to this problem was proposed by Hochreiter & Schmidhuber, adding a

long short-term memory (LSTM) [40]. The intuition behind the LSTM is to control the memory in a

structured way. Therefore, the LSTM can determine whether the content of the memory should be

remembered, updated or forgotten. Long-term dependencies can be recognized in the LSTM network

by training this memory. Evaluation of the proposed LSTM network showed it outperforms the

conventional RNNs in terms of prediction accuracy and required training time.

The architecture of a model predictive controller could be divided in a centralized or a distributed

approach. The first, relies on a central computer to make control decisions and direct the actions of

individual controllers, whereas at the second the intersection controller is responsible for operation

decisions [14]. The centralized control approach is often not feasible due to computational complexity,

communication overhead and lack of scalability [14]. However, distributed approaches are relatively

easy to expand and could reduce the computational complexity. The reduction of the computational

complexity is achieved by a concept developed in SURTRAC [35]. The intention behind this concept is

to model vehicle flows based on a simple model and then aggregate arrivals to create clusters of arriving

vehicles. After, the aggregated arrivals are handled as indivisible jobs in a forward-recursive scheduling

algorithm. The concept resulted in a reduction of the computational complexity that is polynomial in

the prediction horizon.

A variant of the distributed approach is the self-organizing traffic light controller [41]. The objective is

relatively simple, the controller gives preference to cars that have been waiting longer and to larger

groups of cars [15]. In other words, the controller optimizes the traffic light control according to the

cumulative travel time delay. The cumulative travel time delay is calculated for each approaching lane

of the intersection, based on the predicted short-term arrival flows [42]. These arrival flows are

predicted following the Lighthill and Whitham’s fluid dynamic traffic model [36]. The intersection uses

a non-periodic optimization technique to create optimal schedules, which can lead to instability [43].

Therefore, a stabilization mechanism is applied to ensure servicing of each direction as least as good

as a fixed-time strategy [44]. Initially, the controller without a stabilization mechanism was compared

to a fixed-time controller, showing a significant reduction in terms of average queue length and average

travel time delay [42]. In later work, the controller with the stabilization mechanism is compared the

previous controller without the stabilization mechanism, which resulted again in a reduction of the

average travel time delay [44].

2.2. Vehicle detector data approach

It is understood that no matter which control method is used, vehicle data is required as an input to

the traffic light controller. The vehicle data is acquired by a vehicle detector. Any traffic-responsive

control system depends on its ability to sense traffic for local intersection control and / or system-wide

adjustment of timing plans [45]. The detectors are categorized in two different categories: Pavement


Invasive Detector and Non-Pavement Invasive Detector. The first, an in-roadway sensor is one that is

placed as one of the following ways [46]:

• Embedded in the pavement of the roadway.

• Embedded in the subgrade of the roadway.

• Taped or otherwise attached to the surface of the roadway.

The second, an over-roadway sensor is one that is mounted above the surface of the roadway in one

of the following two ways [46]:

• Above the roadway itself.

• Alongside the roadway, offset from the nearest traffic lane by some distance.

This section will cover the detector types presented in Table 2.

Table 2 - Detector types

2.2.1. Inductive-loop detector

Induction loops are the most widely used technology for vehicle detection worldwide [47]. An Inductive-

loop detector consists of one or more turns of wire embedded in the pavement and is connected to a

control box. An inductive-loop detector senses the presence of a conductive metal object by inducing

currents in the object, which reduce the loop inductance [46]. When a vehicle passes over or stops

above the loop, the inductance of the loop is reduced. This causes a detection signal in the control box.

The shape and size of the loop detector depends on the area to be detected, the types of vehicles to

be detected and the objective, such as queue detection, vehicle counting or speed measurements [45].

2.2.2. Magnetic detectors

Magnetic sensors are passive devices that detect the presence of a ferrous metal object through the

perturbation they cause in the Earth’s magnetic field [46]. The perturbation is also known as a magnetic

anomaly. Figure 7 and Figure 8 show the magnetic anomaly created by a ferrous metal vehicle. The

first, indicates how the vector addition of the dipole magnetic field to the quiescent Earth’s magnetic

field produces the magnetic anomaly [46]. The second, shows several dipoles on a vehicle and their

effect on compass readings and sensor output [46].

Pavement Invasive Detector

Inductive loop Microwave Radar Acoustic

Magnetic Detectors Infrared Video Image Processor

Ultrasonic Floating Car Data

Non-Pavement Invasive Detector


Figure 7 - Magnetic anomaly induced in the Earth’s magnetic field by a magnetic dipole [46]

Figure 8 - Perturbation of Earth’s magnetic field by a ferrous metal vehicle [46]

Two types of magnetic field sensors are used to detect traffic: The magnetometer and the magnetic

detector.

Magnetometer

A magnetometer measures the changes in both the horizontal and vertical components of the earth’s

magnetic field caused by a passing vehicle. This is similar to the inductive loop detector, except that it

consists of a coil of wire wrapped around a magnetic core. The magnetometer can detect passing

vehicles, as well as the presence of a vehicle. Magnetometers are useful on bridge decks and viaducts,

where the steel support structure interferes with loop detectors, and loops can weaken the existing

structure [45].

Magnetic detector

The magnetic detector detects the vehicle signature by measuring the distortion in the magnetic flux

lines induced by the change in the Earth’s magnetic field produced by a moving ferrous metal vehicle

[46]. These detectors can only detect moving vehicles, consequently they cannot be used as a presence

detector [45]. However, multiple units of some magnetic detectors can be installed and utilized with

specialized signal processing software to generate vehicle presence data [46].

2.2.3. Microwave radar detector

Microwave radar was developed for detecting objects in the period before and during World War II

[46]. The term radar is an acronym for RAdio Detection And Ranging (RADAR). Originating from military

applications, the radar technology is now also used for traffic data collection. The microware radar

transmitting electromagnetic signals and measures the energy reflected from a vehicle. With this


technique the reflected signal from vehicles can be used to determine presence, passage, volume, lane

occupancy, speed, and vehicle length depending on the waveform transmitted by the radar sensor [46].

The two waveforms used are continuous wave and frequency modulated continuous wave. The first is

known as Doppler radar and can only detect flow and speed [45]. The second can also detect the

presence of a vehicle [45]. Figure 9 shows the transmission of energy by an overhead-mounted

microwave radar toward an area of roadway.

Figure 9 - Microwave radar operation [46]

2.2.4. Infrared detector

The infrared detector could be used in an active or passive state for traffic flow monitoring applications.

The energy captured by active and passive infrared sensors is focused by an optical system onto an

infrared-sensitive material mounted at the focal plane of the optics [46]. The measured data is

processed and analysed to extract the information used utilized for signal control.

Active infrared

The active infrared detector transmits energy and detects the wave that are reflected [45]. This type

provides vehicle count, presence, speed, occupancy, vehicle classification and the detection of

pedestrians.

Passive infrared

The passive infrared detector does not transmit energy, instead they detect energy from two sources

[46]:

• Energy emitted from vehicles, road surfaces, and other objects in their field-of-view.

• Energy emitted by the atmosphere and reflected by vehicles, road surfaces, or other objects

into the sensor aperture.

Passive infrared detectors provide less information compared to the active type. It detects vehicle count,

presence and occupancy.

Vehicle

Microwave

Radar Antenna

Sign bridge,

overpass, pole, or mast arm mounting

Reflected signal from vehicle can be used

to determine presence, passage, volume, lane

occupancy, speed, and vehicle length depending on the waveform transmitted by the radar sensor

Controller

cabinet

Power and

data cables


2.2.5. Ultrasonic detector

The ultrasonic detector is similar to the microwave radar, except it transmits ultrasonic sound energy

waves instead of electromagnetic signals. It measures the distance that the reflected wave travels [45].

These measurements are processed to obtain data of vehicle presence, speed and occupancy.

2.2.6. Acoustic detector

The acoustic detector uses an array of microphones to determine vehicle passage, presence and speed

by measuring acoustic energy or audible sounds [46]. The sounds are produced by vehicular and from

the interaction of vehicle’s tires with the road [45].

2.2.7. Video Image Processor

Video cameras were introduced to traffic management for roadway surveillance based on their ability

to transmit closed-circuit television imagery to a human operator for interpretation [46]. Today a Video

Image Processor (VIP) is used to automatically analyse traffic and extract information. The video

camera can detect traffic and the images of the camera are digitized, processed and converted into

traffic data [45]. A video image processor can provide vehicle count, presence, speed, occupancy and

vehicle class. Video image processors that track vehicles may also have the capability to register turning

movements and lane changes [46].

2.2.8. Floating Car Data

The emergence of floating car systems was made possible by using wireless communication methods,

especially the Global Positioning System (GPS) [48]. The first Floating Car Data (FCD) based application

has evolved in the early 1990’s [48], [49]. Advanced Driver and Vehicle Advisory Navigation Concept

(ADVANCE) was launched in 1991 as a major test of a dynamic, in vehicle route guidance system in

the United States [50]. ADVANCE was first practically implemented in 1997 in the Gary-Chicago-

Milwaukee Corridor transport system, which is considered the first intelligent transport system [51].

The main benefit of floating car data is that every vehicle acts as moving sensor, therefore no additional

hardware is required on the roadway [49], [52]. Furthermore, floating car data benefits from maximum

flexibility as it can be extended over large areas with only a marginal increase in variable costs [53].

In addition, developments in communication and sensor technology create the possibility to send

additional vehicle information next to the existing floating car data. This is the second generation of

floating car data and labelled as Extended Floating Car Data (xFCD) [48]. Possible information xFCD

could provide is shown in Figure 10. xFCD is considered a promising development for future dynamic

traffic management [54].


Figure 10 - Extended FCD examples [55]

Floating car data is part of the Cooperative Intelligent Transport Systems (C-ITS). C-ITS is a concept

in which mobile road users such as vehicles and the road side infrastructure get engaged in mutual

information exchange to align their behaviours and intentions such that traffic conditions can be

optimized [56]. A key enabling technology of C-ITS is wireless vehicle communication, covering vehicle-

to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, infrastructure-to-

vehicle (I2V) communication and could collectively be referred to as V2x communication [57].

The C-ITS environment could be achieved via two connectivity options, Cellular (LTE) and IEEE 802.11p,

also known as WIFI-P (ITS-G5). A third option could be a hybrid concept, a combination of both LTE

and ITS-G5. The communications of both connectivity options are displayed in Figure 11. The key

difference is the direct communications among 802.11p equipped devices and the reliance on the

presence of the network for cellular based [57]. The three options for an C-ITS environment are

discussed in more detail below.

Figure 11 - Comparison between IEEE 802.11p and cellular connectivity pipes to the car [57]

LTE

The first option is LTE. The connectivity over the cellular network, currently 3G and 4G, is already widely

adopted in most countries in the world. Additionally, car manufacturers are today offering connected

car solutions as more and more cars are connected via mobile networks (3G/4G) [56]. These services


are an internet connection for in-car infotainment services, over-the-air updates or connected

navigation. 5G communications will expand the possibilities of what mobile networks can do and extend

applications which are more demanding than can currently be supported by 4G [58]. Figure 12 shows

the development of 5G compared to 4G.

Figure 12 - 5G the development compared to 4G [59]

ITS-G5

The second option is ITS-G5. ITS-G5 is based on the IEEE 802.11p wireless access protocol, which is

an adjusted version of the widespread 802.11 technology, branded as WiFi-p [60], [61]. It defines the

overall vehicular communication protocol stack and matches the requirements for V2X communications

in C-ITS applications [62]. This means that the communication is based on broadcast ad hoc networks

to support direct V2V communication without the need of a communication network infrastructure of

third parties [56]. ITS-G5 is expected to be implemented in new production cars starting in 2019 [63].

Therefore, a large deployment is not realistic in the near future. Hence, smaller scale ITS-G5

deployments targeting specific well-defined groups and business cases are the first focus, for example

[56]:

• V2V communications supporting C-ACC and truck platooning. Such intervehicle applications do

not specifically require the PKI Security framework to be set up. For example, fleet owners can

apply alternative security solutions to allow platooning at least between vehicles of their own

fleet;

• Interactions between intelligent traffic control installations and vehicles belonging to specific

categories (e.g. busses and trams, emergency services, taxi’s, value transport vehicles, etc);

• Traffic light installations can be equipped with a camera and so facilitating ‘around the corner’

warnings also at low ITS-G5 penetration rates;

• Renewal of street light furniture could be combined with the installation of ITS-G5 in these

systems. This could support C-ITS services on traffic junctions where traffic lights are not

used.


Combination of LTE and ITS-G5

Finally, the third option is a combination LTE and ITS-G5. The postponement of introduction of ITS-G5

into new cars to 2019 is an immediate delay factor for large scale deployment of C-ITS [56]. In addition,

the International standards for 5G are announced to be ready in 2020 [56]. The situation as developed

to date and the uncertainties which apply, has initiated discussions about a hybrid approach, leveraging

both ITS-G5 and cellular communications as supporting information channels for C-ITS applications

[56]. A hybrid approach would also allow to combine the strengths of both options. The advantages of

a combined approach are the following [56]:

• The service provider achieves a higher penetration / coverage as it invokes multiple

communication channels via cellular and ITS-G5;

• The cellular connection can provide a fast take-up of suitable services due to the redundant

availability and high adoption of mobile communication services. Hence the dependency on

ITS-G5 is strongly reduced;

• With cellular, the in-car terminal has either this as a primary channel during the low-penetration

stages or has a backup channel in case of interruptions in ITS-G5 reception;

• The cellular connection can be used to accommodate better coverage to support C-ITS security

functions (e.g. exchange of certificate information);

• Multiple uplink channels via cellular and ITS-G5 connectivity are available to drop probe data

which is collected by cars.

2.3. Priority strategy

A traffic light controller equipped to with a priority strategy requires vehicle detections as an input to

grant priority to specific vehicles. Traffic signal priority is in principle the concept of improving service

or reducing delay for specific traffic modes at signalized intersections. Traffic signal control systems

traditionally treat either the aggregated flow of traffic or each mode separately, as summarized in Table

3.

Table 3 - Traffic signal control treatments for different traffic modes [64]

The two most widely implemented traffic signal priority control systems are Emergency Vehicle Signal

Pre-emption (EVSP) and Transit Signal Priority (TSP). A third traffic signal priority control system is

freight signal priority, which can also be described as truck signal priority. Before discussing the

strategies in more detail, it is important clarify the difference of pre-emption and priority. In general,

both methods have the same goal. However, the difference is found in the way they achieve it. Signal

2.3. Priority strategy 19

priority modifies the normal signal operation process to better accommodate transit vehicles, while pre-

emption interrupts the normal process for special events [65]. First, section 2.3.1 describes the

emergency vehicle pre-emption systems. Second, section 2.3.2 elaborates the transit signal priority

methods. Finally, section 2.3.3 explores the truck signal priority.

2.3.1. Emergency Vehicle Signal Pre-emption

Emergency vehicles are considered as a special vehicle type, including ambulances, police vehicles and

fire trucks. These vehicles are the first responders at emergency’s and for this reason are required to

reach their destination as quickly as possible. One of the most time-consuming delays is the travel time

to the emergency location. Especially manoeuvring through signalized intersections causes delays and

involves major safety risks, both for the emergency vehicles and other vehicles at the intersection.

Hence, it is the challenge to ensure safe passage of an emergency vehicle and at the same time to

maintain safe and smooth traffic flow in the road network [66].

Instead of allowing emergency vehicles to pass intersections without following the traffic lights, it is

preferable to provide a green light at intersections. The most used method is Emergency Vehicle Signal

Pre-emption (EVSP) at the signalized intersection [67], [68]. Pre-emption generally involves a control

strategy that immediately switches from the current phase to a pre-selected phase for the first request

received [64]. emergency vehicles could request a signal pre-emption treatment via the following

methods: optical systems [69], acoustic systems [70], inductive loop technology, radio controlled

systems [71] or Global Positioning System (GPS) [72], [73].

In addition, several attempts have been made to reduce the response time of emergency vehicles and

minimize the impact on general traffic. Wang et al. [74] proposed a degree-of-priority based control

strategy for emergency vehicle pre-emption operation. The paper of Qin and Khan [66] reports two

control strategies. First, a real-time control strategy is that enables signal transitioning from normal

operation to emergency vehicle signal pre-emption, in order to provide the approaching emergency

vehicles a safe crossing over the intersection safely at its operating speed. Second, an optimal two-

phase control algorithm, consisting of a relaxation method and a stepwise search strategy, is used for

the signal transitioning back to normal operation. A different approach is proposed by Viriyasitavat and

Tonguz [75], a self-organized traffic control scheme using a different set of local rules at intersections.

As a result, the priority management of emergency vehicles is controlled is a self-organized manner.

2.3.2. Transit Signal Priority

Transit signal priority (TSP) control is widely used at signalized intersections has been recognized as a

practical strategy to improve the efficiency and reliability of transit operations [76]. In contrast to the

emergency vehicle pre-emption strategy, transit signal priority could provide a priority service treatment

within the normal signal operation. Consequently, a transit signal priority strategy has a significantly

lower impact on other traffic modes at an intersection, compared to the emergency vehicle pre-emption

strategy. In addition, transit service is typically much more frequent than emergency vehicle service,


use of priority rather than pre-emption allows the system to maintain a higher level of performance

[65]. Based on different control methods, transit signal priority can be divided into two categories:

Passive priority and active priority.

Passive priority control operates without explicitly recognizing the actual transit vehicle presence and

uses predetermined signal timings to provide benefits to transit vehicles [76]. The timings are based

on historical data and an arrival distribution. Therefore, the success of passive priority strategies

depends on low fluctuation of the traffic volumes and deterministic dwell times of buses at stops [77].

Compared with passive priority methods, active priority systems use upstream detection systems and

can respond to transit vehicles in real time [78]. Approaching transit vehicles are detected and the

signal controller would provide the designated signal control methods. Furthermore, based on different

computational complexities, active priority can be categorized into rule-based priority and model-based

priority.

In a rule-based priority strategy the controller grants priority based on a series of constraints, for

example the presence of a transit vehicle and if the transit vehicle is behind schedule. The rule-based

priority could again be subdivided in unconditional and conditional priority. The general idea behind

unconditional priority is that a transit vehicle should always receive priority when approaching an

intersection, without consideration if the vehicle is behind schedule. In contrast, conditional priority

should consider more conditions to grant priority. In addition, to the actual presence of the transit

vehicle, a conditional priority request is only granted if the vehicle is behind schedule [78]. Additional

rules could be applied to reduce the impact on other traffic modes. First attempts involve limiting the

red truncation frequency, it is only allowed if the previous green was not extended [79]. A similar

concept is to consider the allowance of priority based on granted priority in previous cycles [80], [81].

Development of advanced signal control and traffic detection techniques led to more sophisticated

conditional control to account for the overall benefits of passenger cars and transit vehicles [76].

Model based priority control refers to a relatively new generation of priority schemes, which attempts

to achieve advanced operational objectives by means of adaptive signal control [82]. Possible objectives

are reducing total vehicle delay, total transit vehicle delay and total person delay. These objectives are

optimized in real-time according to performance criteria, which may include person delay, transit delay,

vehicle delay or a combination of the previous [65]. This method requires the controller to collect real-

time data of the intersection, examples are the transit location data and traffic conditions. Based on

the actuated signal control system, a real-time multi-objective transit signal priority controller was

developed [83]. The required input information is collected by the inductive loop detectors and

according to the objective function, which is the weighted summation of transit passenger delay,

passenger car delay and transit schedule delay, the transit signal priority controller is able to reduce the

total person delay of the entire intersection. Another approach is based on the model predictive control

system RHODES [33]. A hierarchical optimization strategy determines the durations of the phases

2.3. Priority strategy 21

considering the total vehicle delay, total transit delay and the transit schedule conditions while providing

transit priority [84].

A summarize of the key characteristics of different transit signal priority controls is found in Table 4.

Table 4 - Key characteristic of different transit signal priority controls [76]

2.3.3. Truck Signal Priority

In contrast to transit signal priority, which has the objective is to reduce transit delays, the objective of

truck signal priority (TkSP) is to reduce the overall traffic delay and environmental impact. Since truck

signal priority aims to provide a green light for approaching trucks, a reduction in the number of stops

could be achieved. As a result, improving the safety at signalized intersections and possibly stimulate

truck drivers to use specific routes.

Similar concepts as the transit signal priority could be applied for truck priority. However, there are

three key differences [85]. First, the arrival frequency of trucks is much higher. Second, the trucks do

not have relatively fixed schedules as transit vehicles. Third, the priority level of trucks is lower.

Consequently, trucks require a different priority approach to avoid a large negative impact on other

traffic modes. Early implementations of truck priority strategies have been specifically designed for high

speed rural intersections [86], [87]. These strategies had the objective to minimize the truck stops and

solely relied on loop detectors for truck detections. Recent developments of new detection solutions

and communication methods provide opportunities for active priority strategies, which require a real-

time detection of approaching vehicles [88]. Examples are truck detections using video cameras [89],

a priority system based on GPS and wireless communication [90], [91] and truck priority using

connected vehicle technology [92].


In recent years, there has been an increasing amount of literature on the development of new truck

priority strategies. Ioannou [85] proposed two different controller strategies for truck signal priority: a

neural network-based controller and one based on integrated priority strategies. The first, uses a neural

network approach to model the vehicle delays based on different classes of vehicles and adaptively

controls the traffic signals. The second, uses a combined passive and active strategy, which grants

priority to truck under predetermined conditions. In contrast to previous mathematical prediction

models, Zhao [88] developed a truck priority system that uses a co-simulation-based optimization

control approach for traffic light control by using real time simulators to predict the traffic state.

Suthaputchakun and Sun [3] propose an adaptive traffic light scheduling scheme via two-way traffic-

light-to-vehicle communication for fuel consumption and carbon dioxide (CO2) emission reduction. In

addition, a priority framework to optimize a weighted traffic light schedule is proposed, by assuming

the weight of a truck two-times higher than a normal vehicle.

2.4. Conclusions

In chapter 2, extensive literature studies have been conducted about signalized intersection controllers

in order to present an overview of the state-of-the-art of signalized intersection controllers. The chapter

started with the different signalized intersection controller approaches. Afterwards, multiple ways of

detecting vehicles were discussed. Finally, the priority strategies were explored. Together these studies

provide important insights into the development of signalized intersections controllers. These will be

highlighted and presented alongside the design choices for each section below.

Section 2.1 presents four main types of signalized intersection controllers. The most promising findings

in the literature on signalized intersection controllers is the development of model predictive control

(MPC). Especially, the Self-Organizing Intersection Controller (SOIC) approach. Moreover, the

combination with traffic flow predictions using Long Short-Term Memory (LSTM) approach showed

major potential. Therefore, for this research an MPC is proposed, more specific the SOIC approach, in

combination with LSTM traffic flow predictions.

Section 2.2 provides an overview of the available vehicle detector data approaches. Literature on this

topic showed a rapid development of connected vehicle techniques. These techniques enable vehicle

detections based on Floating Car Data (FCD). In addition, extensive information could be shared with

the intersections controller by Extended Floating Car Data (xFCD), such as direction, speed and vehicle

type. Accordingly, it is suggested that floating car data will eventually replace conventional detection

technique, such as the inductive-loop detector. For this reason, floating car data will be included in this

research.

Section 2.3 presents three different priority strategies. Most of the research found on the topic of

priority strategy was focused on Emergency Vehicle Signal Pre-emption (EVSP) and Transit Signal

Priority (TSP). However, the research found on truck priority often used EVSP and TSP strategies as a

baseline in the development of their approach. An interesting approach used for truck signal priority in

2.4. Conclusions 23

a traffic-adaptive controller, is a weighted traffic light schedule. This approach allows a traffic light

controller to optimize the light schedule based on the vehicle type. For this reason, it is proposed to

use a weighted traffic light schedule as a basis of the truck signal priority controller in this research.

The selection of the proposed techniques will be used to develop a truck signal priority controller. To

summarize, the proposed truck priority strategy is a MPC. The MPC will have a SOIC approach and will

use a LSTM approach for traffic flow predictions. A floating car data vehicle detection technique will be

used in order to detect different vehicle types. Finally, a weighted traffic light schedule will be used to

enable truck signal priority.

25

3 3. DIRECTOR

This chapter describes the basic principles in the design of the state-of-the-art model predictive

controller DIRECTOR, which are required as preliminary knowledge in this thesis. DIRECTOR has a

Self-Organizing Intersection Controller approach and uses Long Short-Term Memory traffic flow

predictions, which are the proposed techniques defined in the literature review of the previous chapter.

Further, DIRECTOR uses available floating car data to improve the traffic flow predictions. The floating

car data could be extended to provide additional information, e.g. the vehicle type and the Origin-

Destination, to the controller. Given the above, DIRECTOR includes the design choices made in the

previous chapter and could be used as the baseline throughout this research.

The main goal of DIRECTOR is to minimize the average travel time delay per vehicle, while still being

able to provide predictable and stable information on signal changes. Accordingly, the intuition behind

DIRECTOR is to use the cumulative travel time delay, which is based on the predictions of the arriving

traffic flows and the current queue length, to optimize the traffic light schedule and fix the schedule

ahead of time. Ultimately, enabling Advanced Driver Assistance Systems (ADAS), such as Green Light

Optimal Speed Advice (GLOSA). A more detailed description is found in the work of Van Senden [93].

The following sections describe the basic principles of DIRECTOR in an intuitive manner. First, the

cumulative travel time delay is introduced (section 3.1). Subsequently, a uniform distribution of arrivals

is assumed (section 3.2). Thereafter, the mathematical formulation of the schedule decision is

presented and extended with a switching penalty (section 3.3). Finally, the schedule will be fixed ahead

of time (section 3.4). The last section concludes the basic principles in the design of DIRECTOR (section

3.5). The relationship between the different sections is shown in Figure 13.

Figure 13 - Layout chapter 3: DIRECTOR

This chapter will answer the following sub question, presented in section 1.3:

(2) What are the basic principles of the state-of-the-art model predictive controller?

26 3. DIRECTOR

3.1. Cumulative travel time delay

The overall concept of DIRECTOR is based on the cumulative travel time delay, which is calculated by

multiplying the queue length with the waiting time. To illustrate, Figure 14 displays an example of how

a queue evolves in practice for one Origin-Destination. It shows the arrival of vehicles over ten seconds

and the relation between the queued vehicles and the cumulative travel time delay. The blue line, 𝜌(𝑡),

represents the number of queued vehicles and the blue shaded area represents the cumulative travel

time delay of the interval zero to ten, 𝐷𝑂𝐷(0,10). A mathematical form is given by (3.1).

𝐷𝑂𝐷(𝑡𝑠𝑡𝑎𝑟𝑡, 𝑡𝑒𝑛𝑑) = ∫ 𝜌𝑂𝐷

(𝑡)𝑡𝑒𝑛𝑑

𝑡𝑠𝑡𝑎𝑟𝑡

(3.1)

Figure 14 - Arrival of vehicles over 10 seconds [93]

3.2. Uniform distribution of arrivals

However, the future vehicle arrivals are unknown. It is therefore the challenge to estimate the future

arrivals. These vehicles can be predicted within a chosen time period according to a Long Short-Term

Memory traffic flow prediction model, using detector information of surrounding traffic controllers. The

chosen time period is considered as a time bin. It should be noted that the Long Short-Term Memory

does not predict the exact arrival times within the time bin. For this reason and without further regarding

the arrival patterns of vehicles, DIRECTOR assumes a uniform distribution of arrivals. Van Senden [93]

introduces an average queue length over a ten second interval, since it will reduce the computational

complexity required to calculate the travel time delay. This method is similar to the concept of SURTRAC

[35], which reduced the computational complexity by aggregating the arrivals to create clusters of

arriving vehicles. The average queue length will be denoted as 𝜌𝑂𝐷[𝑇], where the index T is a time bin

of a ten second interval. The example of Figure 14 is extended to 20 seconds in Figure 15 and illustrates

the transition from 𝜌(𝑡) to 𝜌[𝑇] at two time bins. For example, 𝜌[0] and 𝜌[1] in Figure 15 correspond

to the average queue length value of 4 and 7.5 respectively.

3.3. Schedule decision 27

Figure 15 - Transition from ρ(t) to ρ[T] [93]

3.3. Schedule decision

According to the arrivals described in the previous section, a schedule decision is made. The scheduled

Origin-Destination at time bin T, denoted as 𝜒[𝑇], is based on the Origin-Destination with the highest

priority at time bin T, denoted as 𝜋[𝑇]. The mathematical formulation of the schedule decision is given

by (3.2).

𝜒[𝑇] = arg max𝑂𝐷∈𝑂𝐷𝑠

{𝜋[𝑇]} = arg max𝑂𝐷∈𝑂𝐷𝑠

{ 𝜌𝑂𝐷[𝑇]} (3.2)

However, an intersection often has several non-confliction Origin-Destinations which can be serviced

simultaneously as a Phase Group. Therefore, the cumulative travel time delays of the Origin-

Destinations within a Phase Group are added together. The representation of the schedule decision

based on the Phase Groups is given by (3.3).

𝜒[𝑇] = arg max𝑃𝐺∈𝑃𝐺𝑠

{𝜋[𝑇]} = arg max𝑃𝐺∈𝑃𝐺𝑠

{ ∑ 𝜌𝑂𝐷

[𝑇]

𝑂𝐷∈𝑃𝐺

} (3.3)

The previous schedule decision (3.3) assumes the possibility of an instant switch between Phase

Groups. However, an instant switch is not allowed for safety reasons. It is required to have a period

between termination of a green signal and the next signal turning green. This period is referred to as

intergreen time. During this time no vehicles can be serviced by the intersection. Hence, switching

phase groups leads to potential inefficiencies at the intersection. Therefore, a switching penalty is

introduced, denoted as σ𝑃𝐺. The idea is that this penalty reduces the priority of the phase groups

currently not being serviced and therefore increases the relative importance of the phase group

currently being serviced. An illustration of the switching penalty is found in Figure 16. The red marked

area (first 4,5 sec) represents the time where no vehicles can be serviced.

28 3. DIRECTOR

Figure 16 – Illustration of the switching penalty [93]

The scheduling decision including the switching penalty is given by (3.4).

𝜒[𝑇] = arg max𝑃𝐺∈𝑃𝐺𝑠

{𝜋[𝑇]} = arg max𝑃𝐺∈𝑃𝐺𝑠

{( ∑ 𝜌𝑂𝐷

[𝑇]

𝑂𝐷∈𝑃𝐺

) − σ𝑃𝐺[𝑇])} (3.4)

3.4. Fixed schedule ahead of time

The schedule decision equation of the previous section responds ad hoc on the predicted arrivals of the

next time bin. However, DIRECTOR aims to provide predictable and stable information on signal

changes, which is not guaranteed in the ad hoc situation. For this reason, DIRECTOR will fix the

schedule ahead of time by using the predicted arrivals in future time bins and the current queue length.

The fixed schedule enables DIRECTOR to provide predictable and stable information on signal changes.

Accordingly, the schedule decision is given by (3.5).

𝜒[𝑇 + 1] = arg max𝑃𝐺∈𝑃𝐺𝑠

{𝜋[𝑇 + 1]} = arg max𝑃𝐺∈𝑃𝐺𝑠

{( ∑ 𝜌𝑂𝐷

[𝑇 + 1]

𝑂𝐷∈𝑃𝐺

) − σ𝑃𝐺[𝑇 + 1]} (3.5)

3.5. Conclusions

This chapter served as the basis for the development of the truck signal priority controller design. The

basic principles and terminology of DIRECTOR are described and will be used in the remainder of this

thesis. DIRECTOR is a state-of-the-art model predictive controller, which is able to provide predictable

and stable information on signal changes. This is accomplished by fixing the schedule ahead of time,

based on the predicted arrivals and the current queue length. To reduce the computational complexity,

an average queue length over a ten second interval is used. Further, a switching penalty is included to

account for the inefficiencies related to switching phase groups. However, DIRECTOR is not able to

make a schedule decision based on different vehicle types. This is considered as an opportunity to

improve the controller. For this reason, the next chapter will propose a new design based on DIRECTOR

with a truck signal priority strategy.

29

4 4. Truck Signal Priority

controller design This chapter describes the design of the Truck Signal Priority (TkSP) controller. The work of this

research is based on the predictive controller DIRECTOR developed by Van Senden [93] described in

chapter 3. The control algorithm is modified and extended with the ability to make a schedule decision

based on different vehicle types. In this research, the main focus is the truck signal priority. For this

reason, only cars and trucks will be part of the study.

The following sections elaborate on the proposed design of DIRECTOR TkSP, starting with a single

Origin-Destination and step by step extend to multiple Origin-Destinations in a Phase Group. First, the

truck arrivals are described for a single Origin-Destination (section 4.1). Subsequently, a set of priority

weights is proposed (section 4.2). Next, the equation to calculate the truck weight for one Origin-

Destination is defined (section 4.3). Thereafter, the new schedule decision including the truck weight

is described (section 4.4). Finally, the Truck Signal Priority controller design is concluded in the last

section (section 4.5). The relationship between the different sections is shown in Figure 17.

Figure 17 - Layout chapter 4: Truck Signal Priority controller design

This chapter will answer the third sub question, presented in section 1.3:

(3) How should the priority weights be assigned to different vehicle types?

4.1. Truck arrivals

Prioritization of specific vehicle types requires the signalized intersection controller to detect different

vehicle types. Literature described multiple detection techniques, which are able to detect different

vehicle types. Especially, floating car data showed potential. The main benefit of floating car data is

that every vehicle acts as moving sensor, therefore no additional hardware is required on the roadway

30 4. Truck Signal Priority controller design

[49], [52]. In addition, floating car data has the possibility to send additional information, such as

direction, speed and vehicle type. Therefore, the method used to detect an approaching truck is based

on floating car data, as proposed in chapter 2. The range of the detection is set to a distance of 330

m, based on the minimum distance between the intersection under control and an upstream intersection

in the network. Alternatively, the selected distance could also be extended to a larger distance, which

enables earlier knowledge of an approaching truck. However, an increased detection distance would

increase the uncertainty of the estimated time of arrival. If a larger detection distance is required, a

truck could update its floating car data more frequently to reduces the uncertainty of the estimated

time of arrival. For simplification is the floating car data of the truck limited to one update throughout

this research. Accordingly, the estimated time of arrival is calculated, which is based on the distance to

the intersection and a speed limit of 50 km/h. Resulting in an estimated time of arrival of approximately

24 seconds. For this reason, three 10 second time bins are proposed, the index of the time bin is

denoted as T. Figure 18 illustrates the point of detection and the corresponding time bins in an approach

of the intersections.

Figure 18 - Point of detection

It should be noted that the count for the time bins starts at zero on the stop line of an intersection and

increases in steps of one moving upstream. The truck arrivals, denoted 𝜏, will be assigned to a time

bin, as shown in Figure 19. Equation (4.1) gives the result of the combined truck arrivals of a single

Origin-Destination.

Figure 19 - Truck arrivals per time bin

4.2. Priority weights

According to the truck arrival time bins, discussed in the previous section, a set of priority weights is

proposed. This set will correspond to the first three time bins, since the trucks have an estimated

arriving time at the intersection within 30 seconds. In the future this set could be extended to more

𝜏[𝑇] = [0, 0, 1] (4.1)

4.2. Priority weights 31

time bins. However, it should be noted that if more time bins are used the uncertainty would increase

as well. The set of priority weights will be denoted as 𝜔. Table 5 illustrates the index corresponding to

the time bin, which follows the same structure as defined for the truck arrivals in the previous section.

Table 5 - Priority weight index to corresponding time bin

𝜔 [0] 𝜔 [1] 𝜔 [2]

Time [s] (0,10) (11,20) (21,30)

The assigned weights are based on the differences in vehicle characteristics. Suthaputchakun and Sun

[3] make a connection between different types of vehicles based on their actual weight. For example,

heavily loaded vehicles normally have higher emissions and consume more fuel. For this reason,

Suthaputchakun and Sun assumed that the weight of a heavily loaded vehicle is two-times higher than

that of the small vehicles. However, more vehicle characteristics are found in the literature. For instance,

the length of a truck is around 1.5 to 4 times the length of a standard car [10]. Further, significant

differences are found in the vehicle dynamics, especially in the acceleration rates after a complete stop.

A typical truck has an acceleration rate around five times lower compared to a passenger car, when

accelerating to 50 km/h [11]. Combining the vehicle characteristics above, an assumption is made on

the impact of a truck at an intersection compared to a standard car. The following characteristics are

taken into consideration to determine the impact: weight (2), length (4) and acceleration rate (5). The

values correspond to the number of times a truck has more impact compared to a standard car.

However, not all defined vehicle characteristics are taken as equal. It is assumed that the acceleration

rate of a truck has two times more impact on the intersection. Since, all cars stopped behind a truck

are limited to the acceleration rate of the truck in front. The weighted average of the vehicle

characteristics above result that a truck has four times more impact at the intersection compared to a

standard car. Hence, an extra weight of four is given to an arriving truck at a specific time bin. A truck

is required to anticipate on red light earlier than a regular car, due to their slow dynamics. Therefore,

it is assumed that a truck will experience delays, if the signal is not green in the ten seconds before a

truck arrives. The extra weight will be added to the second time bin to ensure a smooth passage for a

truck crossing the intersection. Consequently, the set of priority weights is given in (4.2).

𝜔[𝑇] = [1, 4, 1] (4.2)

However, queues could already exist in front of an arriving truck. In the same way as stopping for a

red light, a truck has to anticipate earlier on a queue. To account for the possible queues and clear

them before a truck is arriving, a second weight is added in the set of priority weights. However, this

second priority weight should not be equal to the value of the second time bin. Since, it is intended to

clear a queue in advance and not give early green. The value of the additional weight is therefore

proposed to be half of the value of the second time bin. It follows that the priority weight is added in

the third time bin, because this is the farthest time bin away from the intersection. Finally, the proposed

set of priority weights is found in (4.3).

32 4. Truck Signal Priority controller design

𝜔[𝑇] = [1, 4, 2] (4.3)

4.3. Truck weights

Following the previous sections, all the required is available to calculate the truck priority weight,

denoted as 𝛿. The basic idea is to multiply the truck arrival by the assigned priority weight, given by

(4.4).

𝛿 = 𝜏 ∗ 𝜔 (4.4)

However, one or more approaches of the intersection could have multiple Origin-Destinations. For this

reason, it is required to receive the Origin-Destination from the floating car data of the truck. In

addition, multiple trucks could approach the intersection on the same Origin-Destination. To find the

truck weight of one Origin-Destination, the weight of the approaching trucks should be summed.

Accordingly, (4.4) is rewritten to add the weights of three time bins together. The calculation of the

truck priority weight of a single Origin-Destination is described by (4.5).

𝛿𝑂𝐷[T] = ∑ 𝜏[𝑇] ∗ 𝜔[𝑇]

2

𝑇=0

(4.5)

Example 4.3.1.

According to (4.5) an example is presented. In this example, three Origin-Destinations will be reviewed

and the 𝛿𝑂𝐷[T] will be calculated based on the inputs given by Table 6 and Table 7. The results of the

example are presented in Table 8. As can be seen in the results, the highest truck priority weight is

found for Origin-Destination number two. It should be noted that Origin-Destination number three

receives a lower truck priority weight, despite having three instead of two trucks approaching the

intersection. This is because of the arrival times of the trucks. The first truck arrives in the first time

bin and does not receive additional priority weights, since it already experienced delays. For this reason,

the results are as expected.

Table 6 - Priority weights example 4.3.1.

𝜔[𝑇]

[1, 4, 2]

Table 7 - Truck arrivals example 4.3.1.

OD 𝜏[𝑇]

1 [0, 0, 1]

2 [0, 1, 1]

3 [1, 0, 2]

Table 8 - Results example 4.3.1.

OD 𝛿𝑂𝐷[T]

1 2

2 6

3 5

4.4. Schedule decision 33

4.4. Schedule decision

The previous sections limits the calculation of the truck weight to a single Origin-Destination. However,

multiple Origin-Destinations are often not-conflicting and can be serviced simultaneously. The set of

combined Origin-Destinations is described as a Phase Group. To find the truck weight of a Phase Group,

denoted as 𝛿𝑃𝐺, all Origin-Destinations within the Phase Group are summed up following (4.6).

𝛿𝑃𝐺[T] = ∑ ∑ 𝜏[𝑇] ∗ 𝜔[𝑇]

2

𝑇=0𝑂𝐷∈𝑃𝐺

= ∑ 𝛿𝑂𝐷[T]

𝑂𝐷∈𝑃𝐺

(4.6)

Finally, the schedule decision described by (3.5) in chapter 3 could be extended by (4.6) to find the

new schedule decision, including the extra weights for approaching trucks. The mathematical

formulation of the final schedule decision is described by (4.7).

𝜒[𝑇 + 1] = arg max𝑃𝐺∈𝑃𝐺𝑠

{ ∑ 𝜌𝑂𝐷[𝑇 + 1]

𝑂𝐷∈𝑃𝐺

− σ𝑃𝐺[𝑇 + 1] + 𝛿𝑃𝐺[𝑇 + 1]}

= arg max𝑃𝐺∈𝑃𝐺𝑠

{ ∑ 𝜌𝑂𝐷[𝑇 + 1]

𝑂𝐷∈𝑃𝐺

− σ𝑃𝐺[𝑇 + 1] + ∑ 𝛿𝑂𝐷[𝑇 + 1]

𝑂𝐷∈𝑃𝐺

}


{( ∑ 𝜌𝑂𝐷[𝑇 + 1] + 𝛿𝑂𝐷[𝑇 + 1]

𝑂𝐷∈𝑃𝐺

) − σ𝑃𝐺[𝑇 + 1]}

(4.7)

4.5. Conclusions

This chapter proposed a new controller design with a Truck Signal Priority (TkSP) strategy. The goal

was to develop a controller design that is able to make a predicted schedule decision based on different

vehicle types and give priority to trucks. In order to reach the goal, different steps have been taken.

First, the truck arrivals have been specified to corresponding time bins for a single Origin-Destination.

Second, the priority weights were assigned to the defined time bins individually. The value of the

weights was assumed based on the impact of a truck at an intersection compared to a standard car.

To determine the impact the following vehicle characteristics were taken into consideration: the weight,

the length and the acceleration rate. However, not all defined vehicle characteristics were taken as

equal. It was assumed that the acceleration rate had two times more impact compared to the weight

and length. Consequently, the value was determine by a weighted average. A second value was

introduced to account for possible queues in front of an arriving truck, which was proposed to be half

of the value of the previous time bin. Subsequently, the truck weights were formulated for a single

Origin-Destination. Finally, the truck weights were determined for a Phase Group and combined with

the schedule decision of chapter 3. This resulted in the new schedule decision with the truck signal

priority strategy and completed the design of DIRECTOR TkSP.

35

5 5. Experimental setup

This chapter describes the experimental setup used to evaluate the performance of the proposed

controller in Chapter 4. There are two options to evaluate the performance of the proposed controller.

The first options is to evaluate the performance of the proposed controller with live traffic on the street.

However, this could lead to unsafe situations at the intersection. In addition, evaluation with live traffic

is not reproducible. The results obtained would therefore not give an equal comparison between

different controller configurations. The second option is to use a simulation environment. The simulation

environment provides a controlled environment, which allows to reproduce traffic flows and give an

equal comparison between different controller configurations. Further, the simulation environment

could create the simplified intersection geometry defined in section 1.4. Given the above, it is clear that

a simulation environment is the best option in this research and will be used to evaluate the

performance of the proposed controller.

The chapter starts by introducing a case study (section 5.1). Thereafter, the simulation tools required

to create, run and evaluate the performance of the controller are discussed (section 5.2). Subsequently,

the data required to run a simulation will be identified (section 5.3). Afterwards, the simulation model

is described in detail, including the intersection geometry, the communication to the controller and the

implemented measurements (section 5.4). Finally, the experimental setup is concluded in the last

section (section 5.5). The relationship between the different sections is shown in Figure 20.

Figure 20 - Layout chapter 5: Experimental setup

This chapter will answer the fourth sub question, presented in section 1.3:

(4) How could the intersection controller with priority weights be evaluated?


5.1. Case study

The proposed controller of Chapter 4 is tested within a case study. The case study will use an

intersection that is located in the Netherlands near Hoofddorp in the province of Noord-Holland. The

name of the intersection is N201 – Verbindingsweg N205 and the number is 201234. The intersection

has a T-shape geometry and connects the N205 and N201. It has a feed of three surrounding

intersections. Each intersection is at driving distance of approximately thirty seconds. These

intersections are Spaarnepoort, Leenderbos and Verbindingsweg, and correspond to the numbers

201231, 201209 and 205195 respectively. Further, a simplification of the geometry is used in this

research. The lanes for pedestrians and cyclist are left out the network. Therefore, the intersection

under control has six Origin-Destinations and could serve multiple vehicle types. However, only two are

currently under investigation: cars and trucks. Each Origin-Destination has an own identification number

according to Dutch traffic conventions, which is why the identification numbers 1, 5 and 9 are not

present, as seen in Figure 21. This particular intersection was chosen by Helmy [38] based on the

following reasons: First, the availability of all required detectors and the lack of influential sources and

sinks. Second, representatives from the Province of Noord-Holland were quite supportive of this project

and were willing to provide any resources needed. Another reason to continue with the location is that

it is the first site where the province of Noord-Holland has agreed to test DIRECTOR on the street.

Figure 21 - Schematic overview of the case study intersection.

5.2. Simulation tools

The case study presented in the previous section is modelled in a simulation environment. Multiple

simulation tools are required to create, run and evaluate the performance of the controller. This section

will introduce the tools used for the simulation. First, the simulation software program PTV Vissim,

described in section 5.2.1. Second, in section 5.2.2 is the programming language Python introduced,

which is used to write the controller algorithm.

5.3. Simulation data 37

5.2.1. PTV Vissim

PTV Vissim is a powerful tool for traffic simulation and the main program used by Dutch Road owners

[94]. Therefore, one reason for use is that most intersections in the Netherlands are already created in

PTV Vissim. Another reason is the possibility of extensive evaluation and visualization methods. Finally,

a large part of the case study is available for evaluation in PTV Vissim and requires minor adjustments

for the implementation of the new controller.

PTV Vissim is mainly used through its graphical interface for simulation, evaluation and visualization

purposes. Additionally, PTV Vissim also provides a COM (Component Object Model) interface, which

enables control of functions in PTV Vissim via programming. A few examples of these functions are

adjusting simulation settings, vehicle behaviour, evaluation during or after a simulation run and signal

control. There are several programming languages that could be used in the COM interface e.g. C++,

Visual Basic or Python. The version of PTV Vissim used in this research is 10.00-12 (64 bit).

5.2.2. Python

The programming language used for this research is Python, which has been used in the previous work

of DIRECTOR. Continuing to use Python made it possible to implement the new design, without

rewriting the complete control algorithm. Python in combination with the COM interface has been the

main method to control the simulations in PTV Vissim and test the controller.

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics

[95]. A major benefit of Python is that both the standard library and the interpreter are available free

of charge. Python supports the use of modules and packages, which enables program modularity and

code reuse. The modules and packages used in this research are found in Appendix B.

5.3. Simulation data

The simulation tools defined in section 5.2 require data of vehicle arrivals to run a simulation and

evaluate the controller performance. The aim of the simulation environment is to reflect the reality as

close as possible. For this reason, historic data of the intersection is used in the simulation to evaluate

the controller in a close to real scenario. However, the historic data does not contain information on

the vehicle type. Therefore, additional information on the truck arrivals is required. First, section 5.3.1

will elaborate on the historic data. After, section 5.3.2 describes how the additional information on the

truck arrivals is obtained.

5.3.1. Historic data

The historic data is received from the province of Noord-Holland and is provided in a V-Log data format.

A format in which Dutch traffic light controllers log their data [93]. The V-Log data only records changes

in the data stream, which contains the detectors states, signals states and the internal system state of

the traffic light controller. The historic data used in the simulation dates from January 2017 to May


2017 and is compiled from the V-Log format to numerical data that can be used for machine learning

and simulation purposes [93]. The data set of vehicle arrivals is found in Appendix C.

5.3.2. Truck data

The data compiled from the V-Log data does not contain information on the vehicle type. Therefore, it

is required to create a realistic data set of truck arrivals. The truck arrival data is estimated from the

original detector data. This data contains the timestamps of when a detector becomes occupied and

when the detector is free again. These timestamps are compared to find the occupation time of a

detector. In this occupied time data is a three-point median search conducted to find expected truck

detections. If the median is equal or larger than nine seconds, then the point is marked as a truck

detection. Figure 22 shows the truck detections, marked with red dots, for a single Origin-Destination

in 24 hours. These truck detections are saved to a new data file, in the same structure as the original

detections, to use as an input for the truck simulations. The data set of truck arrivals is found in

Appendix C.

Figure 22 - Truck detections (marked with red dots)

5.4. Simulation model

The PTV Vissim model for the intersection of the case study presented in section 5.1 is available and

provided by the province of Noord-Holland. However, this model contains a larger network of multiple

intersections, which are not used in this research. Although they are not actively used in the simulation,

they use computational power of the computer to simulate the vehicles in the network. Therefore, all

unnecessary elements in the PTV Vissim network are removed to have an optimal network for a smooth

simulation. Subsequently, the model is adjusted to be able to replay historical data of vehicle arrivals.

The locations of the vehicle inputs are shown in Figure 23. Further, multiple vehicle detectors are used

in the model. These are displayed as blue rectangles in Figure 23. During the simulation the controller

reads the states of the detectors every 100 milliseconds. The change between a state of the detector

is used to count vehicles passing a detector. As each lane has two detectors, i.e. the arrival detector

5.4. Simulation model 39

and stop line detector, the controller can count arriving and departing vehicles. Combining information

of two detectors on the same lane enables the controller to calculate current queue length of each

Origin-Destination pair. Further, the controller is able to change the state of the signal heads in PTV

Vissim, which are shown as red strip in Figure 23. Due to limitations of the simulation software the

changes are limited to once every second. Subsequently, measurement points are added for each

individual Origin-Destination, in order to evaluate the controller on the following outputs:

- Number of stops per vehicle - Vehicle delay [s]

The measurements points are vehicle travel time detectors in PTV Vissim. This detector type measures

the time it takes a vehicle to travel from one point to the next. The locations of the travel time detectors

are displayed in Figure 23, where the pink line is the start of the measurement and the green line is

the end of the measurement. The following outputs can be calculated with these measurements: the

vehicle travel delay, the number of stops and the number of vehicles passing. A delay of a vehicle is

calculated when the actual travel time is compared to the travel time it would need under free flow

conditions. Free flow conditions are considered as for example when the vehicle can maintain its desired

speed, i.e. without reacting on another vehicle or a red signal. In addition, each individual Origin-

Destination of the measurement could be specified for the different vehicle types. Hence, evaluations

could be reviewed for three configurations: all vehicles, only cars and only trucks.

Figure 23 - PTV Vissim network layout of the intersection

Further, node evaluations are used to determine the exhaust emissions. The basis for the emission

calculations are formed by standard formulas for consumption values, as well as data on emissions

incorporated in PTV Vissim. The data refers to a typical North American vehicle fleet and does not

differentiate between individual vehicle types. As can be seen in Figure 23, one rectangular node is

used and includes the entire intersections. Four outputs will be obtained through the evaluations:

carbon monoxide (CO) emissions, nitrogen oxide (NOx) emissions, Volatile Organic Compounds (VOC)

emissions and fuel consumption. The first three are measured in grams and the fuel consumption is

measured in US liquid gallon.


5.5. Conclusions

This chapter presented an experimental setup, which is developed to evaluate the performance of the

proposed controller with priority weights. A case study is used based on an existing intersection in the

province of Noord-Holland, the Netherlands. Historic data of vehicle detections enabled the case study

to evaluate the controller in a close to real scenario. However, the truck arrivals were extracted from

the original historic data, due to the unavailability of historic truck data. The layout of the intersection

was modelled in the traffic simulation program PTV Vissim. Via the COM interface could the controller,

written in the programming language Python, control the signal states in the simulation environment.

Several measurement points were added to evaluate the performance, based on the number of stops

per vehicle and the vehicle delay. Further, node evaluations were used to determine the exhaust

emissions and fuel consumption. Finally, the experimental setup is complete and able to evaluate the

performance of the proposed controller with priority weights.

41

6 6. Simulation results

This chapter describes and discusses the results from the simulations of the case study presented in

the previous chapter. Multiple weight configurations will be simulated to evaluate the performance of

the Truck Signal Priority controller. The following sections present the results in an intuitive manner.

First, a comparison is made between the baseline and the proposed weights of chapter 4 (section 6.1).

Subsequently, a sensitivity analysis is performed to determine an optimal configuration of priority

weights (section 6.2). Thereafter, the results are compared for a full week (section 6.3). Afterwards,

an emission analysis is described for three different priority weights configurations (section 6.4). Finally,

the obtained results are discussed (section 6.5). The relationship between the different sections is

shown in Figure 24.

Figure 24 – Layout chapter 6: Simulation results

This chapter will answer the following sub questions, presented in section 1.3:

(5) Does a change in priority weights affect the performance in terms of traffic flow?

(6) Can a model predictive controller with priority weights improve the performance of a state-of-

the-art model predictive controller in terms of reducing the number of stops?

(7) How does the controller perform at different traffic demands?

6.1. Truck Signal Priority performance

To evaluate the performance of the new design for truck signal priority, simulations following the case

study of the previous chapter are conducted. These simulations include two sets of different weights

for trucks. The first, is the baseline of [1, 1, 1]. In this configuration no extra weight is added to a truck

in the schedule decision. The second, is the proposed set of [1, 4, 2]. In this configuration, extra weight

is added to a truck in the schedule decision. Here, a truck is equal to four cars in the second time bin

and equal to two cars in the third time bin. Both configurations are simulated with a data set of 24

hours and have the same vehicle inputs, presented in Table 9. The simulations are evaluated according


the Key Performance Indicators (KPI), described in section 1.2. These are the number of stops and the

vehicle delay. Both can be further specified, by dividing the key performance indicators in three vehicle

groups. Each group is a different configuration of vehicle types. The first contains all the vehicles, which

is a mix of cars and trucks. The second contains only cars and likewise the third contains only trucks.

The results of the two simulations are found in Figure 25, Figure 26 and Figure 27. The graphs present

the results per Origin-Destination for each vehicle group, which are: All, Cars and Trucks respectively.

Further, the key performance indicators are found in the graphs. The left axis is the percentage of stops

and the right axis is the average vehicle delay in seconds. The total number of vehicles per vehicle

group, as well as the number of vehicles per Origin-Destination, are presented in Table 9.

As can be seen in Figure 25 and Figure 26 the stop percentages of the vehicle groups All and Car have

a minor difference. However, a more distinct difference, with a range of 2% to 19%, is noticed in Figure

27 for the different Origin-Destinations. This indicates that the priority weights have a different impact

on the stop percentage for each Origin-Destination. A similar trend is found for the average vehicle

delay. The results of the simulation with the priority weights show a decrease in average vehicle delay

in comparison to the baseline. Finally, the total stops and the total vehicle delay, for both simulations,

are shown in Table 10 and Table 11 respectively. The lowest values for each vehicle group are

highlighted yellow, which quickly shows similar results to the comparison per Origin-Destination. The

simulation with priority weights has 87 stops less for trucks and 265 stops more for cars. Further, the

total vehicle delay for trucks is reduced by 45 minutes (2673 seconds) and increased by 111 minutes

(6642 seconds) for cars.

Given the above, the introduction of priority weights for trucks has a positive influence on the key

performance indicators for the trucks. However, it has a minor negative impact for the cars.

Table 9 - Number of vehicles: 24 hours

Origin-Destination Total

2 3 4 6 7 8

All 12405 4979 5648 6463 7054 11505 48054

Cars 12304 4865 5458 6226 6983 11381 47217

Trucks 101 114 190 237 71 124 837

Table 10 – Total stops

Total stops

All Car Truck

[1, 1, 1] 23239 22750 489

[1, 4, 2] 23417 23015 402

Table 11 – Total vehicle delay

Total vehicle delay [s]

All Car Truck

[1, 1, 1] 653197 637582 15615

[1, 4, 2] 657166 644224 12942

6.1. Truck Signal Priority performance 43

Figure 25 - Comparison: All

Figure 26 - Comparison: Cars

Figure 27 - Comparison: Trucks


6.2. Priority weights sensitivity analysis

As can be seen in the previous section, the use of priority weights has an positive influence on the

performance. Hence, it is expected that a change in the priority weights would change the performances

as well. To analyse the sensitivity of the priority weights a Design of Experiments (DoE) is proposed.

This method aims to describe the results under a variation of conditions. By introducing one change in

the set of priority weights it is expected to result in a change in one or more output variables. Following,

the priority weights are chosen as input variables. The output variables contain the key performance

indicators, as described in section 6.1 and include the number of stops and vehicle delay. Based on the

results described in the previous section, a design of experiments is proposed with the following

changes of input variables: The set of priority weights will start at [1, 1, 1] and will change the variables

one by one. The first time bin is always one, as described in section 4.2. The other two will vary in a

range of eight values, starting at one. The used weight data set is illustrated in Table 12.

Table 12 – Priority weight data set

[1, 1, 1] [1, 2, 1] [1, 3, 1] [1, 4, 1] [1, 5, 1] [1, 6, 1] [1, 7, 1] [1, 8, 1]

[1, 1, 2] [1, 2, 2] [1, 3, 2] [1, 4, 2] [1, 5, 2] [1, 6, 2] [1, 7, 2] [1, 8, 2]

[1, 1, 3] [1, 2, 3] [1, 3, 3] [1, 4, 3] [1, 5, 3] [1, 6, 3] [1, 7, 3] [1, 8, 3]

[1, 1, 4] [1, 2, 4] [1, 3, 4] [1, 4, 4] [1, 5, 4] [1, 6, 4] [1, 7, 4] [1, 8, 4]

[1, 1, 5] [1, 2, 5] [1, 3, 5] [1, 4, 5] [1, 5, 5] [1, 6, 5] [1, 7, 5] [1, 8, 5]

[1, 1, 6] [1, 2, 6] [1, 3, 6] [1, 4, 6] [1, 5, 6] [1, 6, 6] [1, 7, 6] [1, 8, 6]

[1, 1, 7] [1, 2, 7] [1, 3, 7] [1, 4, 7] [1, 5, 7] [1, 6, 7] [1, 7, 7] [1, 8, 7]

[1, 1, 8] [1, 2, 8] [1, 3, 8] [1, 4, 8] [1, 5, 8] [1, 6, 8] [1, 7, 8] [1, 8, 8]

The results of the simulations are visualized in Figure 28 and Figure 29. The graphs show the difference,

between the baseline and the weight configuration, for the key performance indicators. The left axis is

the number of stops and the right axis is the vehicle delay in seconds. To clarify, a positive number of

stops indicates an increase in the number of stops compared to the baseline. More detailed information

on the number of stops and the total vehicle delay is found in Appendix E.

Figure 28 – Sensitivity analysis: 24 hours (1/2)

6.2. Priority weights sensitivity analysis 45

Figure 29 – Sensitivity analysis: 24 hours (2/2)

The overall objective is to reduce the number of stops for trucks. However, the implementation of

priority weights should have a minimal impact on the traffic flow of other vehicles. Figure 29 shows the

higher weight configurations, which reduces the number of stops for trucks compared to the baseline.

On the other hand, the number of stops for cars are increasing for higher weight configurations. Similar

results are found for the vehicle delay. For this reason, the results presented in Figure 29 do not meet

the objective. The lower weight configurations, presented in Figure 28, show promising results. Multiple

weight configurations show a decrease in the vehicle delay, compared to the baseline for both cars and

trucks. In addition, three weight configurations also show a reduction in the number of stops. Especially

the weight configuration [1, 3, 4]. This is also noticed Table 13 and Table 14, which present the total

stops and vehicle delay for three weight configurations: the baseline, the first proposed and the weight

configuration [1, 3, 4]. Compared to the first proposed weights, the new weight configuration reduced

the number of stops for a truck even more, to 344 total stops and the total vehicle delay is decreased

to 206 minutes (12362 seconds). Moreover, the number of stops and total vehicle delay for the other

vehicle groups are also decreasing. This concludes that the weight configuration [1, 3, 4] has an overall

better performance than the first suggested weights. Subsequently, the new weight configuration is

compared to the baseline. Based on the previous comparison it was clear it would outperform the

baseline in terms of the number of stops for trucks. In fact, the results showed that the weight

configuration [1, 3, 4] also outperforms the baseline for the other vehicle groups. The new weight

configuration reduced the total stops by 183 for cars and 143 for trucks. Further, the total vehicle delay

for cars is decreased by 59 minutes (3558 seconds) and 54 minutes (3253 seconds) for trucks.

Table 13 – Total stops: Sensitivity analysis

Total stops All Car Truck

[1, 1, 1] 23239 22750 489

[1, 4, 2] 23417 23015 402

[1, 3, 4] 22911 22567 344

Table 14 - Total vehicle delay: Sensitivity analysis

Total vehicle delay [s] All Car Truck

[1, 1, 1] 653197 637582 15615

[1, 4, 2] 657166 644224 12942

[1, 3, 4] 646386 634024 12362


Following the same approach as section 6.1, the new weight configuration is compared in more detail.

Accordingly, the weight configuration [1, 3, 4] is compared per Origin-Destination with the baseline

configuration [1, 1, 1] and the first proposed configuration [1, 4, 2]. The comparisons for the vehicle

groups All, Cars and Trucks are found in Figure 30, Figure 31 and Figure 32 respectively.

Figure 30 - Sensitivity analysis comparison: All

Figure 31 - Sensitivity analysis comparison: Cars

Figure 32 - Sensitivity analysis comparison: Trucks

6.3. Robustness check 47

Again, comparisons of the vehicle groups All and Car, Figure 30 and Figure 31 respectively, show a

minor difference. While the comparison in Figure 32 shows a more distinct difference in the key

performance indicators. Especially in the decrease in the percentage of stops. Further, the average

vehicle delay is lower for both priority weight configurations compared to the baseline. Although, the

decrease in average vehicle delay is less evident between weight configurations [1, 4, 2] and [1, 3, 4].

Finally, it can be concluded the weight configuration [1, 3, 4] has an overall better performance, in

terms of the key performance indicators, compared to the baseline and other weight configurations.

However, the simulations are limited to 24 hours and one set of vehicle inputs.

6.3. Robustness check

The previous section performed a sensitivity analysis for the priority weights. However, it was limited

to 24 hours and one set of vehicle inputs. Different sets of vehicle inputs should be evaluated to check

the robustness of the weight configuration [1, 3, 4], found in the previous section. Therefore, six

additional days have been simulated, each having a different set of vehicle inputs of 24 hours. The sets

vehicle inputs can be found in Appendix C. The simulation results of the seven days are added together,

in order to evaluate the performance over a longer period. The number of vehicles over a week are

presented in Table 15. Subsequently, the results could be evaluated for a complete week, as shown in

Figure 33 and Figure 34. The results for the individual days can be found in Appendix D. The overall

objective is similar to the previous section, which quickly reduces the number of suitable weight

configurations. In fact, only one weight configuration meets the objective, [1, 2, 1]. Compared to the

baseline it reduces the total stops by 77 for cars and 88 for trucks, and decreases the total vehicle

delays for cars by 61 minutes (3673 seconds) and 66 minutes (3957 seconds) for trucks. Although, the

weight configuration [1, 2, 1] meets the objective, the results are not impressive. Since it only reduced

the total stops for trucks by 88 over a week. However, the weight configuration [1, 3, 4] shows a more

significant reduction in the total number of stops for trucks over a week, while the increase in the total

number of stops for cars remains minimal.

Figure 33 – Sensitivity analysis: Week (1/2)


Figure 34 – Sensitivity analysis: Week (2/2)

Alternatively, the priority weight configuration [1, 3, 4] could also be evaluated for the individual days.

For instance, Figure 40 and Figure 42, found in Appendix D, show an decrease in total vehicle delay for

cars, while Figure 43 and Figure 44 show an increase in vehicle delay for cars. In the same way, an

increase in the total stops for cars is found in Figure 41 and Figure 43. Further, Figure 44 shows only

a minor deviation to the baseline, compared to the other simulations. However, it should be noted that

this simulation has the least vehicle inputs. The above suggest that the performance of the priority

weight configuration, in terms of total stops and vehicle delay, is depended on the traffic demand.

Finally, it can be concluded that the weight configuration [1, 3, 4] reduces the total of stops for trucks

by 751 over a week, compared to the baseline [1, 1, 1]. However, the total stops for cars increases by

155. As can be seen in Table 17, the total vehicle delay over a week shows a similar trend as the total

stops. Namely, the total vehicle delay for trucks is decreased by 304 minutes (18227 seconds) and

increased by 201 minutes (12033 seconds) for cars. Given the above, it is noticed that both the total

stops and the total vehicle delay decreases more for trucks than it increases for cars. As a result, the

vehicle group All shows an improvement of the key performance indicators for the weight configuration

[1, 3, 4], compared to the baseline.

Table 15 - Number of vehicles: Week

Origin-Destination Total 2 3 4 6 7 8

All 81954 30138 35030 41641 44135 73159 306057

Cars 81405 29582 34007 40300 43781 72499 301574

Trucks 549 556 1023 1341 354 660 4483

Table 16 – Total stops: Week

Total stops All Car Truck

[1, 1, 1] 142225 139404 2821

[1, 3, 4] 141629 139559 2070

Table 17 - Total vehicle delay: Week

Total vehicle delay [s] All Car Truck

[1, 1, 1] 3987494 3897966 89528

[1, 3, 4] 3981301 3909999 71301

6.4. Emission analysis 49

6.4. Emission analysis

In addition to the key performance indicators, it is also interesting to evaluate the performance of the

truck signal priority controller based on emissions and fuel consumption. Therefore, an emission

analysis is conducted for the following priority weights configurations: the baseline [1, 1, 1], the first

proposed configuration [1, 4, 2] and the overall best performing configuration [1, 3, 4]. Similar to the

section 6.3, a complete week will be evaluated based on seven individual days. Further, the emission

analysis will focus on the following outputs of the simulations: carbon monoxide (CO), nitrogen oxide

(NOx), Volatile Organic Compounds (VOC) and fuel consumption. These outputs are compared for each

weight configuration and presented in Figure 35, Figure 36, Figure 37 and Figure 38 respectively. The

data of the emission analysis can be found in Appendix F.

Figure 35 - Carbon monoxide emissions

Figure 36 – Nitrogen oxide emissions

Figure 37 - Volatile Organic Compounds emissions

Figure 38 - Fuel consumption

The figures above show a decrease in emissions and fuel consumption for the priority weight

configuration of [1, 3, 4], which was expected since the total stops decreased as well. In the same

way, an increase in the total stops for the weight configuration [1, 4, 2], shows an increase in the

emissions and fuel consumption. Further, it should be noted that the differences in the results between

the priority weight configurations are relatively small. Since, it was expected that the reduction in the

total stops for trucks would have a more distinct impact, by reducing the emissions and fuel

consumption.


6.5. Discussion

The following section will discuss the results of the previous sections; the truck signal priority

performance evaluation, the priority weights sensitivity analysis, robustness check and the emission

analysis.

6.5.1. Truck Signal Priority performance

The expected results for adding priority weights are confirmed by the results. Compared to the baseline

[1, 1, 1], the weight configuration [1, 4, 2] shows a decrease in stops and vehicle delay for trucks.

However, cars experience a minor increase in the total stops and the vehicle delay. Therefore, the

overall performance is negatively impacted in terms of total stops and total vehicle delay. This can be

explained because there are more cars in relation to trucks in the simulation. To find if it is possible to

reduce the number of stops and vehicle delay for trucks, without negatively impacting other vehicles,

more priority weight configurations should be evaluated.

6.5.2. Priority weights sensitivity analysis

By performing a design of experiments to analyse the sensitivity of the priority weights, it was expected

to find a relation between the priority weights configuration and the key performance indicators. Figure

29 shows the higher weight configurations, which reduces the number of stops for trucks compared to

the baseline. On the other hand, the number of stops for cars are increasing for higher weight

configurations. Similar results are found for the vehicle delay. It follows that the higher priority weight

configurations do not have the desired results, since they negatively impact the traffic flow.

Another interesting result is noticed in Figure 28 and Figure 29, which shows that it is more beneficial

to have a higher value for the third time bin in combination with a lower value for the second time bin.

This finding indicates the importance to clear a possible queue in front of the arriving truck. In order

to cross the intersection without a stop. However, the weight values 6, 7 and 8 in the third time bin

also show a major increase in the totals stops and total vehicle delay for cars. This may indicate that

the trucks are simply given too much priority weight before they arrive at the intersection. On the other

hand, it could also indicate that a weight is added to clear a queue, while no queue is present. A possible

solution would be to first check the current queue and only add the priority weights if a queue is

present.

Further, Figure 28 shows a clear reduction for the priority weight configuration [1, 3, 4] based the key

performance indicators, for both cars and trucks. For this reason, the weight configuration [1, 3, 4] is

added in the comparison of section 6.1, which showed an improvement for all vehicle groups compared

to the first proposed priority weight configuration [1, 4, 2]. Moreover, a decrease in the total stops and

total vehicle delay was also noticed compared to the baseline [1, 1, 1]. Finally, it can be concluded the

weight configuration [1, 3, 4] has an overall better performance, in terms of the key performance

indicators, compared to the baseline and other weight configurations. However, the simulations are

6.5. Discussion 51

limited to 24 hours and one set of vehicle inputs. Therefore, different sets of vehicle inputs should be

evaluated to check the robustness of the priority weight configuration [1, 3, 4].

6.5.3. Robustness check

Different sets of vehicle inputs were evaluated to check the robustness of the weight configuration

[1, 3, 4]. Therefore, the simulation results of the seven days were added together, in order to evaluate

the performance over a longer period. Subsequently, the results could be evaluated for a complete

week. The results showed one priority weight configuration that met the objective, which was

[1, 2, 1]. However, the results were not impressive. Since it only reduced the total stops for trucks by

88 over a week. On the other hand, the weight configuration [1, 3, 4] showed a more significant

reduction in the total number of stops for trucks over a week, while the increase in the total number of

stops for cars remained minimal. Subsequently, the priority weight configuration [1, 3, 4] was evaluated

for the individual days. The evaluation showed that the performance of the priority weight configuration,

in terms of total stops and vehicle delay, is depended on the traffic demand. This indicates that is would

be beneficial to have an dynamic priority weight configuration, which would adapt to the current traffic

demand. Another approach could be to implement of multiple priority weight configurations for each

Origin-Destination. The priority weight configurations could then be optimized per Origin-Destination,

since the traffic demand is often different for each Origin-Destination.

However, the results for the priority weight configuration [1, 3, 4] showed an overall improvement of

the key performance indicators over a week. Despite, increasing the total stops and total vehicle delay

for cars. It can be concluded that the proposed truck signal priority controller design can reduce the

number of stops for trucks at a signalized intersection, while maintaining the overall traffic flow at least

as good as a state-of-the-art model predictive intersection controller.

6.5.4. Emission analysis

In addition to the key performance indicators, an emission analysis was conducted to evaluate the

performance of DIRECTOR TkSP based on emissions and fuel consumption. The results of the emission

analysis showed a similar trend as the result of the key performance indicators. As can be seen in the

figures of section 6.4. These show a decrease in emissions and fuel consumption for the priority weight

configuration of [1, 3, 4], which was expected since the total stops decreased as well. In the same

way, an increase in the total stops for the weight configuration [1, 4, 2], shows an increase in the

emissions and fuel consumption. Further, it should be noted that the differences in the results between

the priority weight configurations are relatively small. Since, it was expected that the reduction in the

total stops for trucks would have a more distinct impact, by reducing the emissions and fuel

consumption. However, the available version of PTV Vissim, for this research, was not able to calculate

vehicle type specific emissions and fuel consumptions, which resulted in equal emissions for cars and

trucks. Hence, the evaluation can only be used to compare the emissions of different scenarios. For

this reason, the impact of a decrease in the total stops for trucks was not visible in the results.

53

7 7. Conclusions and recommendations

7.1. Conclusions

The research described the development of a new truck signal priority controller, to address the

increasing freight transport and improve the road traffic sustainability. The goal was to reduce the

number of stops for trucks at signalized intersections by granting priority to trucks via implementation

of priority weights for different vehicle classes. The implementation of this controller was expected to

reduce the number of stops for trucks and improve the traffic flow at signalized intersections. An

extensive literature study resulted in an overview of the state-of-the-art of signalized intersection

controllers. Findings in the literature provided the required techniques to develop the controller. The

proposed truck priority strategy was a Model Predictive Controller (MPC) with a Self-Organizing

Intersection Controller (SOIC) approach and used a Long Short-Term Memory (LSTM) approach for

traffic flow predictions. The detection of different vehicle types was based on Floating Car Data (FCD)

and a weighted traffic light schedule in combination with priority weights was used to enable truck

signal priority.

The proposed design was evaluated according to a case study, which was based on an intersection in

the province of Noord-Holland. The case study evaluated different configurations of priority weights

with the traffic simulation tool PTV Vissim. Historic data of the intersection was used in the simulation

to evaluate the controller in a close to real scenario. Each simulation provided detailed results, including

the vehicle delay and the number of stops. Further, node evaluation provided results on the following

emissions: carbon monoxide (CO) emissions, nitrogen oxide (NOx) emissions, Volatile Organic

Compounds (VOC) emissions and fuel consumption.

The results of this research showed that the implementation of priority weights for trucks has an

influence on the performance of the intersection controller, in terms of number of stops and vehicle

delay. Early results presented a comparison of a proposed priority weight configuration to a baseline

with no priority weights. Simulating a full day, it was found that the simulation with priority weights

54 7. Conclusions and recommendations

has 87 stops less for trucks and 265 stops more for cars (17,8% and 1,16% respectively). Further, the

total vehicle delay for trucks is reduced by 45 minutes and increased by 111 minutes for cars (17,1%

and 1,0% respectively). A sensitivity analysis was performed to further study the effects of the priority

weights. The results showed that the priority weight configuration [1, 3, 4] outperforms the baseline.

The total stops were reduced by 143 for trucks and 183 for cars (29,67% and 0,8% respectively), also

the total vehicle delay for truck was decreased by 54 minutes and 59 minutes for cars (0,6% and 20,8%

respectively).

Subsequently, a robustness check was performed to evaluate the performance of the priority weight

configuration over a week, compared to the baseline. The results showed an reduction of the total

stops by 751 and total vehicle delay by 304 minutes for trucks (26,6% and 20,4% respectively).

However, the total stops and total vehicle delay for cars increased, by 155 stops and 201 minutes (0,1%

and 0,3% respectively). The node evaluations, included in PTV Vissim, used for the emission analysis

were not vehicle type specific. Hence, the impact of the reduced total number of stops for trucks was

not visible in the results.

Finally, the results for the priority weight configuration [1, 3, 4] showed an overall improvement of the

key performance indicators over a week. The overall total stops and total vehicle delay were reduced

by 596 stops and 103 minutes (0,42% and 0,16% respectively). Despite, increasing the total stops and

total vehicle delay for cars. To answer the research question, a model predictive control-based

signalized intersection controller with priority weights can lead to a reduction in the number of stops

for trucks at a signalized intersection, while maintaining traffic flow at least as good as a state-of-the-

art model predictive controller.

7.2. Recommendations

Based on the results, several research directions could be indicated to improve the truck signal priority

controller design. However, due to the limited time available in this research these are considered part

of future work. The following recommendations can be made for future work:

- A future study might explore adding a dynamic priority weight configuration. It is shown that

the optimal priority weights configurations is influenced by the current traffic demand.

Therefore, machine learning is suggested to adjust the priority weights according to the current

traffic situation in order to maintain an optimal performance.

- Another approach to improve the performance of the controller could be the implementation

of multiple priority weight configurations for each Origin-Destination. The priority weight

configurations could then be optimized per Origin-Destination, since the traffic demand is often

different for each Origin-Destination.

7.2. Recommendations 55

- Further research can be done with a more advanced emission evaluation program. One that is

recommended is the add-on module EnViVer, which is based on the VERSIT+ exhaust

emissions model from TNO. This module enables to determine pollutant emissions based on

vehicle trajectories and other information from PTV Vissim.

- It would be interesting to see the effects of Green Light Optimal Speed Advice (GLOSA).

Currently, the controller only receives Floating Car Data (FCD) from the vehicles. Based on the

calculated schedule the controller could send GLOSA information back to the vehicles. It is

expected that the implementation of GLOSA will reduce the number of stops even more, for

both cars and trucks.

- This research is limited by using two types of road users. It would be interesting to see how

the controller performs with more types of road users. More research is required to incorporate

other road users into the simulation to further approach the reality.

- A future study might explore adding weights to other road users. For example, it could be

beneficial to give more priority to cyclist in city centres. Hence, it would stimulate the use of a

bicycle and discourages the use of cars.

57

References

[1] Nunzio, G.D., “Trafc eco-management in urban trafc networks,” Université Grenoble Alpes, 2015.

[2] Kaparias, I., Zavitsas, K., & Bell, M.G.H., “State-Of-The-Art of Urban Traffic Management Policies

and Technologies,” CONDUITS, Coordination Of Network Descriptors for Urban Intelligent Transport Systems, Imperial College London, 2010.

[3] Suthaputchakun, C. & Sun, Z., “A Novel Traffic Light Scheduling Based on TLVC and Vehicles’

Priority for Reducing Fuel Consumption and CO2 Emission,” IEEE Systems Journal, vol. 12, no. 2, pp. 1230-1238, 2018.

[4] Wu, L., Yusheng, C., Chu, J. & Zhang, H., “The Influence of Intersections on Fuel Consumption in Urban Arterial Road Traffic: A Single Vehicle Test in Harbin,” PLoS One, China, 2015.

[5] Brilon, W. & Zurlinden, H., “Ueberlastungswahrscheinlichkeiten und Verkehrsleistung als Bemessungskriterium fuer Strassenverkehrsanlagen (Breakdown Probability and Traffic

Efficiency as Design Criteria for Freeways),” Forschung Strassenbau und Strassenverkehrstechnik, Heft 870, 2003.

[6] Azar, C., Lindgren, K. & Andersson, B.A., “Global energy scenarios meeting stringent CO2 constraints—cost-effective fuel choices in the transportation sector,” Energy Policy, Volume 31, Issue 10,, pp. 961-976, 2003.

[7] Essen, Van E., “The Environmental Impacts of Increased International Road and Rail Freight

Transport, Past trends and future perspectives,” in Global Forum on Transport and Environment in a Globalising World 10-12 November 2008, OECD, ITF, Guadalajara, Mexico, 2008.

[8] Metz, N., “World Wide Emission Trend 1950 to 2050 Road Transport and all Sources,”

Eurochamp Workshop in Andechs, Chemistry, Transport and Impacts of Atmospheric Pollutants With Focus on Fine Particulates, EMITRADE-Herrsching, Germany, 2005.

[9] Metz, N., “Estimation of worldwide CO-, NMVOC- NOX- and PM-Emissions,” Proceedings of the

10th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, EMITRADE-Herrsching, Germany, 2005.

[10] Garber, N.J. & Hoel, L.A., Traffic & Highway Engineering, 4th edition, Cengage Learning, ch. 3, 2008.

[11] Institute of Transportation Engineers, Traffic Engineering Handbook, Washington DC: 5th Edition, ch. 5, 2000.

[12] Guberinić, S. & Minic, S.M., “Signal group: Definitions and algorithms.,” YUJOR. Yugoslav Journal of Operations Research. 3., 1993.

[13] Berg, M. van den, “Integrated Control of Mixed Traffic Networks using Model Predictive Control,” Ph.D. thesis, Delft University of Technology, Delft, The Netherlands, 2010.

[14] Katwijk, R.V., “Multi-Agent Look-Ahead Traffic-Adaptive Control.,” Delft University of Technology, Delft, (2008).

58 References

[15] Nunzio, G.D., “Traffic Eco-management in Urban Traffic Networks.,” Université Grenoble Alpes, 2015.

[16] Little, J., “The Synchronization of Traffic Signals by Mixed-Integer Linear Programming,” Operations Research, vol. 14, no. 4, pp. 568–594,, 1966.

[17] Little, J., Kelson, M. & Gartner, N., “MAXBAND. A Versatile Program for Setting Signals on Arteries and Triangular Networks,” Transportation Research Record: Journal of the Transportation Research Board, vol. 795,, 1981.

[18] Gartner, N., Assmann, S., Lasaga, F. & Hou, D., “A Multi-Band Approach to Arterial Signal

Optimization Traffic,” Transportation Research Part B: Methodological, vol. 25B, no. 1, pp. 55–74,, 1991.

[19] Gartner, N. & Stamatiadis, C., “MULTIBAND-96: A Program for Variable Bandwidth Progression Optimization of Multiarterial Traffic Networks,” Transportation Research Record, vol. 1554, pp. 9–17,, 1996.

[20] Robertson, D.I., “TRANSYT: A Traffic Network Study Tool, Road Research Laboratory Report, 1969.”.

[21] Maslekar, N., Mouzna, J., Boussedjra, M. & Labiod, H., “CATS: An adaptive traffic signal system based on car-to-car communication. J. Netw. Comput. Appl. 2013, 36, 1308–1315”.

[22] Hoogendoorn, S. & Knoop, V., “Traffic Flow Theory and Modelling; Edward Elgar Publishing Limited: Cheltenham, UK, 2012”.

[23] Zheng, X., Recker, W. & Chu, L., “Optimization of Control Parameters for Adaptive Traffic-Actuated Signal Control,” J. Intell. Transp. Sys, vol. 14, p. 95–108, 2010.

[24] Zheng, X. & Chu, L., “Optimal Parameter Settings for Adaptive Traffic-Actuated Signal Control,”

in In Proceedings of the International IEEE Conference on Intelligent Transportation Systems, Beijing, China, 12–15 October, 2008.

[25] Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A. & Wang, Y., “Review of Road Traffic Control Strategies, Proceedings of the IEEE, vol. 91, no. 12, pp. 2043–2067, 2003.”.

[26] Blokpoel, R. & Niebel, W., “Advantage of Cooperative Traffic Light Control Algorithms,” ITS European Congress., 2016.

[27] Feng, Y., Head, K.L., Khoshmagham, S. & Zamanipour, M., “A real-time adaptive signal control

in a connected vehicle environment,” Transp. Res. Part C Emerg. Technol., vol. 55, no. 55, p. 460–473., 2015.

[28] Hunt, P.B., Robertson, D.I., Bretherton, R.D. & Winton, R.I., “SCOOT A Traffic Responsive Method of Co-Ordinating Signals,” TRRL Laboratory Report 1014, 1981.

[29] Lowrie, P.R., “The Sydney Coordinated Adaptive Traffic System (SCATS) - Principles, Methodology, Algorithms,” in International Conference on Road Traffic Signaling, 1982.

[30] Mauro, V. & Taranto, C., “Utopia,” in IFAC Proceedings Volumes Control, Computers, Communications in Transportations, 1989.

[31] Gartner, N., “OPAC: A Demand Responsive Strategy for Traffic Signal Control,” Transportation Research Record, vol. 906, p. 75–81, 1983.

[32] Henry, J.J., Fargas, J. & Tuffal, J., “The PRODYN Real Time Traffic Algorithm,” 4th IFAC-IFICIFORS Conference on Control in Transportation Systems, p. 305–310, 1984.

References 59

[33] Sen, S. & Head, K. L., “Controlled Optimization of Phases at an Intersection,” Transportation Science, vol. 31, no. 1, pp. 5-17, 1997.

[34] Boillot, F., Blosseville, J.M., Lesort, J.B., Motyka, V., Papageorgiou, M. & Sellam, S., “Optimal Signal Control of Urban Traffic Networks,” Road Traffic Monitoring, 1992.

[35] Xie, X., Smith, S.F., Lu, L. & Barlow, G.J., “Scheduledriven intersection control,” Transportation Research Part C: Emerging Technologies, vol. 24, p. 168–189, 2012.

[36] Lighthill, M.J. & Witham, G.B., “A theory of traffic flow on long crowded roads,” In Proceedings of the Royal Society A. On kinematic waves II, vol. 229, p. 317–345, 1955.

[37] Pacey, G.M., “The Progression of a Bunch of Vehicles Released from a Traffic Signal,” Road Research Laboratory, Berkshiye, 1956.

[38] Helmy, N., “An intelligent traffic flow progression model for predictive control applications,” Msc thesis, Delft University of Technology, Delft, 2017.

[39] Hochreiter, S., “Untersuchungen zu dynamischen neuronalen netzen,” Msc thesis, Technische Universität München, München, 1991.

[40] Hochreiter, S. & Schmidhuber, J., “Long short-term memory,” Neural computation, vol. 9, pp. 1735-1780, 1997.

[41] Lämmer, S., Donner, R. & Helbing, D., “Anticipative control of switched queueing systems,” The European Physical Journal B, vol. 3, no. 63, p. 341, 2008.

[42] Lämmer, S. & Helbing, D., “Self-control of traffic lights and vehicle flows in urban road networks,” Journal of Statistical Mechanics: Theory and Experiment, no. 04, 2008.

[43] Kumar, P.R. & Seidman, T.I., “Dynamic instabilities and stabilization methods in distributed real-

time scheduling of manufacturing systems,” IEEE Transactions on Automatic Control, vol. 3, no. 35, p. 289–298, 1990.

[44] Lämmer, S. & Helbing, D., “Self-stabilizing decentralized signal control of realistic, saturated network traffic,” Santa Fe Institute Working Paper, Santa Fe, 2010.

[45] Gordon, R.L. & Tighe, W., “Traffic Control Systems Handbook,” Transportation Management Federal Highway Administration, Washington, 2005.

[46] Klein, L.A., Mills, M.K. & Gibson, D.R.P., “Traffic Detector Handbook: Third Edition—Volume I,” Federal Highway Administration, Georgetown Pike, 2006.

[47] Manasse, H., “Level of Service estimation with in-vehicle sensor floating car data,” Technische Universiteit Delft, ITS Edulab, Delft, 2013.

[48] Banach, S., “Betrouwbaarheid en toepassingen van Floating Car Data,” Technische Universiteit Eindhoven (TUe), Design & Decision Support Systems (DDSS), Eindhoven, 2013.

[49] Tiedong, W. & Jinging, H., “Applying Floating Car Data in Traffic Monitoring,” 2014 IEEE International Conference on Control Science and Systems Engineering, Yantai, China, 2014.

[50] Turksma, S., “The various uses of floating car data,” in Tenth International Conference on Road Transport Information and Control, (Conf. Publ. No. 472) pp. 51-55, London, UK, 2000.

[51] Sen, A., Condie, H., Sheffey, A., Tian, X. & Zhu, X., “ADVANCE Advanced Driver and Vehicle Advisory,” University of Illinois, Chicago, 1996.

60 References

[52] Fabritiis, C. de, Ragona, R. & Valenti, G., “Traffic Estimation And Prediction Based On Real Time

Floating Car Data,” in Proceedings of the 11th International IEEE, Conference on Intelligent Transportation Systems, Beijing, China, 2008.

[53] Fedra, K., Greppin, H., Haurie, A., Hussy, C., Dao, H. & Kanala, R., “GENIE: An integrated environmental information and decision support system for Geneva. Part I: Air quality,” Archives des Sciences, vol. vol. 49, pp. p. 247-263, 1996.

[54] Böhm, M. & Scheider, T., “Extended Floating Car Data in Co-operative Traffic Management,”

International Series in Operations Research & Management Science, volume 144, 161-170,, New York, NY, 2010.

[55] Huber, W., Lädke, M. & Ogger, R., “Extended floating-car data for the acquisition of traffic information,” BMWgroup, Munich, 1999.

[56] Ende, B. van den, Sambeek, M. van, Berkers, F., Broeck, W. van den, & Sluis, J. van de,

“Assessment of wireless connectivity options in support of ITS,” Nederlandse Organisatie voor toegepast-natuurwetenschappelijk onderzoek (TNO), The Hague, The Netherlands, 2016.

[57] Filippi, A., Moerman, K., Daalderop, G., Alexander, P.D., Schober, F. & Pfliegl, W., “Ready to roll: Why 802.11p beats LTE and 5G for V2x,” NXP Semiconductors, Cohda Wireless, and Siemens, 2016.

[58] Kombate, D. & Wanglina, “The Internet of Vehicles based on 5G Communications,” IEEE

International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, 2016.

[59] ITU-R , “IMT Vision – Framework and overall objectives of the future development of IMT for

2020 and beyond,” International Telecommunication Union - M Series: Mobile, radiodetermination, amateur and related satellite services, Geneva, 2015.

[60] Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE, Std 802.11-2012, 2012.

[61] Intelligent Transport Systems (ITS); European Profile Standard for the Physical and Medium Access Control Layer of Intelligent Transport Systems Operating in the 5 GHz Frequency Band, ETSI, Std ES 202 663, 2009.

[62] Lyamin, N., Deng, Q. & Vinel, A., “Study of the platooning fuel efficiency under ETSI ITS-G5 communications,” 016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 551-556, Rio de Janeiro, 2016.

[63] Ende, B. van den, Sambeek, M. van, Berkers, F., Broeck, W. van den, & Sluis, J. van de,

“Assessment of wireless connectivity options in support of ITS - An investigation into and assessment of ITS-G5 based and cellular communications for ITS infrastructure support,”

Nederlandse Organisatie voor toegepast-natuurwetenschappelijk onderzoek (TNO), The Hague, The Netherlands, 2016.

[64] He, Q., Head, K.L. & Ding, J., “Multi-modal traffic signal control with priority, signal actuation and coordination,” Transportation Research Part C: Emerging Technologies, vol. 46, pp. 65-82, 2014.

[65] Baker, R.J., Collura, J., Dale, J.J., Head, K.L. & Hemily, B., “An Overview of Transit Signal Priority,” ITS America, Washington, DC, 2002.

[66] Qin, X. & Khan, A.M., “Control strategies of traffic signal timing transition for emergency vehicle preemption,” Transportation Research Part C: Emerging Technologies, vol. 25, pp. 1-17, 2012.

References 61

[67] Deeter, D., Zarean, H.M. & Register, D., “Rural ITS Toolbox,” 2001.

[68] Federal Highway Administration (FHWA), “Traffic Signal Preemption for Emergency vehicles: A Cross-Cutting Study,” Federal Highway Administration (FHWA), 2006a.

[69] Hunter-Zaworski, K. & Danaher, A., “NE Multnomah street Opticom bus signal priority pilot study,” Final Report TNW97-03, Seattle, Wash, USA,, 1995.

[70] Trafc Technologies LLC, “Sonem 2000 digital siren detector,” Technical Document for Installation, Trafc Technologies, LLC, 2001.

[71] Hashim, Z., “Traffic Light Control System for Emergency Vehicles Using Radio Frequency,” OSR Journal of Engineering, vol. 3, no. 7, pp. 43-52, 2013.

[72] Bullock, D., Morales, J. & Sanderson, B., “Evaluation of emergency vehicle signal preemption on the Route 7 Virginia corridor,” Federal Highway Administration, Richmond, Va, USA, 1999.

[73] Bullock, D. & Nelson, E., “Impact evaluation of emergency vehicle preemption on signalized corridor operation,” Proceedings of the TRB Annual Meeting, Transportation ResearchBoard, Washington, DC, USA, 2000.

[74] Wang, J., Ma, W. & Yang, X., “Development of Degree-of-Priority Based Control Strategy for

Emergency Vehicle Preemption Operation,” Discrete Dynamics in Nature and Society, p. 10, 2013.

[75] Viriyasitavat, W. & Tonguz, O.K., “Priority Management of Emergency Vehicles at Intersections Using Self-Organized Traffic Control,” IEEE Vehicular Technology Conference (VTC Fall), Quebec City, QC, 2012.

[76] Lin, Y., Yang, X., Zou, N. & Franz, M., “Transit Signal Priority Control at Signalized Intersections:

A Comprehensive Review,” Transportation Letters The International Journal of Transportation Research. 7., 2014.

[77] Christofa, E. & Skabardonis, A., “Trafic signal optimization with application of transit signal priority to an isolated intersection,” Transportation Research Record, no. 2259, pp. 192–201, 2011.

[78] Furth, P.G. & Muller, T.H.J., “Conditional bus priority at signalized intersections: better service quality with less traffic disruption,” Transportation Research Record. 1731. 23-30., 2000.

[79] Evans, H. & Skiles, G., “Improving public transit through bus preemption of traffic signals,” Eno Transportation Foundation, 24, (4), 53l–543., 1970.

[80] Allsop, R.E., “Priority for buses at signal-controlled junctions: some implications for signal timings,” Transportation and Traffic Theory, 1977.

[81] Cottinet, M., Breteque, A., Henry, J.J. & Gabard, F., “Assessment by observation and by

simulation studies of the interest of different methods of bus preemption at traffic lights,” in Proc. Int. Symp.on Traffic Systems, Vol.2, pp. 92–l05., Berkeley, CA, 1980.

[82] Shalaby, A., Lee, J., Greenough, J., Hung, S. & Bowie, M., “Development, Evaluation, and Selection of Advanced Transit Signal Priority Concept Directions,” Journal of Public Transportation. 9. 97-120., 2006.

[83] Chang, G.L., Vasudevan, M. & Su, C.C., “Modeling and evaluation of adaptive bus-preemption

control with and without automatic vehicle location system,” Transportation Research Part A: Policy and Practice,, vol. 30, no. 4, pp. 251-268, 1996.

62 References

[84] Mirchandani, P.B., Knyazyan, A., Head, K.L. & Wu, W., “An Approach Towards the Integration

of Bus Priority, Traffic Adaptive Signal Control, and Bus Information/Scheduling Systems,” Computer-Aided Scheduling of Public Transport, Springer-Verlag, Germany, 2001.

[85] Ioannou, P., “Design and Evaluation of Impact of Traffic Light Priority for Trucks on Traffic Flow,” University of Southern California, Electrical Engineering – Systems, Los Angeles, CA, 2015.

[86] Sunkari, S.R., Charara, H. & Urbanik, T., “Minimizing Truck Stops at High-Speed Rural Signalized Intersections,” Institute of Transportation Engineers, Washington, DC, 2001.

[87] Bonneson, J., Middleton, D., Zimmerman, K. & Charara. H., “Intelligent Detection-Control system for High Speed Rural Signalized Intersections,” Texas Transportation Institute, Texas, 2002.

[88] Zhao, Y. & Ioannou, P., “A Traffic Light Signal Control System with Truck Priority,” IFAC (International Federation of Automatic Control), Los Angeles, United States, 2016.

[89] Saunier, N., Sayed, T. & Lim, C., “A prototype system for truck signal priority using video

sensors,” Conference and Exhibition of the Transportation Association of Canada - Transportation in a Climate of Change, 2009.

[90] Liao, C. & Davis, G.A., “Bus signal priority based on GPS and wireless communications phase 1 – simulation study,” University of Minnesota, Department of Civil Engineering, Minnesota, 2006.

[91] Li, F., Wang, D.H., Wang, J. & Jin, S., “An approach of transit passive priority with transit phase

overlapped at intersection of arterial signal progression,” IEEE Conference on Intelligent Transportation Systems, 729-733, 2008.

[92] Kari D., Wu G. & Barth, M.G., “Eco-friendly freight signal priority using connected vehicle technology: a multi-agent systems approach,” IEEE Intelligent Vehicles Symposium, 1187-1192, 2014.

[93] van Senden, J.C., “DIRECTOR: Enabling advanced driver assistance systems with predictive

signalized intersection control using LSTM networks,” Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Embedded Software Section, Delft, The Netherlands, 2018.

[94] PTV GROUP, "Brochure: PTV Vissim," [Online]. Available: http://vision-

traffic.ptvgroup.com/fileadmin/files_ptvvision/Downloads_N/0_General/2_Products/2_PTV_Vissim/BRO_PTV_Vissim_EN.pdf. [Accessed 8 03 2018].

[95] Python, “Python.org,” Python Software Foundation , [Online]. Available: https://www.python.org/doc/essays/blurb/. [Accessed 17 07 2018].

63

A A. Scientific paper

1

Improvement of road traffic sustainability by implementation of priority weights for trucks in

predictive signalized intersection control L. Haanstra¹, ing. A.P. Verhoeven², dr. Ir. X. Jiang¹, dr.ir. H. Polinder¹

¹Delft University of Technology, faculty of 3mE, Department of Transport Engineering and Logistics, Delft,

The Netherlands

²Siemens Mobility BV, Zoetermeer, The Netherlands

Abstract: In the European Union Road freight transport volume is expected to grow 78% between 2000 and

2030, which results in more trucks on the road network. The worldwide estimated trend shows an increase of

150 million freight vehicles and an increase of 240 million passenger vehicles. The growth of both vehicle

classes will have a major impact on the road network and the roads will become congested. Especially in dense

urban environments with many intersections. Further, trucks have a detrimental impact on traffic flows,

especially at intersections, because of their slow dynamics and large size. In addition, a stopping truck results

in higher emissions and fuel consumption compared to a car. However, today’s traffic controllers are not

capable of optimizing traffic flow at intersections based on classification of different vehicles. It would be

beneficial to all vehicles involved if the number of stops for trucks would be reduced to a minimum, by servicing

each vehicle class in a different way. Therefore, a new signalized intersection controller that can reduce the

number of stops for trucks is developed. The controller grants priority to trucks via implementation of priority

weights for different vehicle classes and reduced the number of stops for trucks, while maintaining the overall

traffic flow at least as good as a state-of-the-art model predictive intersection controller.

Keywords: Signalized intersection controller, Truck Signal Priority, Simulation, Optimization.

I. Introduction

Signalized intersections play an important role in

modern society. The introduction of signalized

intersection controllers gave structure and a way of

automatic traffic handling at intersections. Only with

the economic growth that developed countries faced

an exponentially increasing demand for personal

mobility occurred [1]. It quickly resulted in

congestions at signalized intersections in urban

environments. Congestion involves queuing, lower

speeds, and increased travel times, which impose

costs on the economy and generate multiple impacts

on urban regions and their inhabitants [2]. To

eliminate the congestion, signalized intersections

could be replaced with bridges, tunnels or non-

signalized roundabouts. This option is however in

many cases, both economically and spatially, not

feasible [1]. One alternative option to improve

efficiency of urban intersections would be the

innovation of the on-street traffic controllers.

However, traffic inefficiencies will still occur, because

of the disruption of traffic flow caused by a red light,

even with the latest innovations of traffic controllers.

In addition, a reaction to an unanticipated switch

from green to amber causes safety concerns, as

drivers may suddenly stop or quickly accelerate. Apart

from the disruption of traffic flow and safety

concerns, the acceleration and deceleration

behaviour cause the largest amount of fuel

consumption and CO2 emissions [3]. These stops

result in over 50% of the fuel consumption of a

vehicles trip [4]. Moreover, one stop of a vehicle could

create a backward moving shockwave that induces a

cyclic driving state of acceleration and idling. This

behaviour is responsible for up to two thirds of the

total energy loss at intersections [3], [4]. Moreover,

once the traffic light turns green, the inability for

drivers to anticipate when they should accelerate

from stop, and the time it takes to accelerate to free-

flow speed, results in a queue discharge rate that can

be as low as 75% of the road’s capacity [5]. In

addition, the time for a heavy truck to respond to a

traffic light, accelerate and cross the intersection is

much higher than that of normal passenger cars [11].

Despite the different dynamics between trucks and

passenger cars, current traffic controllers do not

service them different. Instead, the traffic controllers

will service both as equal. Hence, the problem could

be described as:

“Today’s traffic controllers are not capable of

optimizing traffic flow at intersections based on

classification of different vehicles.”

2

It is therefore the goal to reduce the number of stops

for trucks, by servicing vehicles based on vehicle type

in future signalized intersection controllers.

Literature

Several techniques are found in the literature to

develop a new signalized intersection controller with

truck signal priority, which are described below. First,

a controller type is elaborated. Second, a detection

method is described. Third, a priority handling

mechanism is presented.

Model predictive control (MPC) is in the basics an

extended version of the adaptive controller. The key

difference is similar to the difference between

actuated and adaptive, the extension of the vision

horizon. The predictive controller has the vision

horizon extended to at least the exit flows of the

previous intersection Consequently, the controller

must be able to handle the large uncertainties

associated with forecasting traffic flow progression.

by Hochreiter & Schmidhuber, adding a Long Short-

Term Memory (LSTM) [6]. The intuition behind the

LSTM is to control the memory in a structured way.

Therefore, the LSTM can determine whether the

content of the memory should be remembered,

updated or forgotten. Long-term dependencies can

be recognized in the LSTM network by training this

memory. A variation of the MPC is the self-organizing

traffic light controller [7]. The objective is relatively

simple, the controller gives preference to cars that

have been waiting longer and to larger groups of cars

[1]. In other words, the controller optimizes the traffic

light control according to the cumulative travel time

delay. The intersection uses a non-periodic

optimization technique to create optimal schedules,

which can lead to instability [8]. Therefore, a

stabilization mechanism is applied to ensure servicing

of each direction as least as good as a fixed-time

strategy [9]. Initially, the controller without a

stabilization mechanism was compared to a fixed-

time controller, showing a significant reduction in

terms of average queue length and average travel

time delay [10]. In later work, the controller with the

stabilization mechanism is compared the previous

controller without the stabilization mechanism, which

resulted again in a reduction of the average travel

time delay [9].

Literature described multiple detection techniques,

which are able to detect different vehicle types.

Especially, Floating Car Data (FCD) showed potential.

The main benefit of FCD is that every vehicle acts as

moving sensor, therefore no additional hardware is

required on the roadway [11], [12]. Furthermore,

FCD benefits from maximum flexibility as a large scale

FCD system can be extended over large areas with

only a marginal increase in variable costs [13]. In

addition, developments in communication and sensor

technology create the possibility to send additional

vehicle information next to the existing FCD. This is

the second generation of FCD and labelled as

Extended Floating Car Data (xFCD) [14].

Suthaputchakun and Sun [3] propose an adaptive

traffic light scheduling scheme via two-way traffic-

light-to-vehicle communication (TLVC) for fuel

consumption and CO2 emission reduction. In

addition, a priority framework to optimize a weighted

traffic light schedule is proposed, by assuming the

weight of a truck two-times higher than a normal

vehicle.

The selection of the proposed techniques will be used

to develop a truck signal priority controller. To

summarize, the proposed truck priority strategy is a

MPC. The MPC will have a SOIC approach and will use

a LSTM approach for traffic flow predictions. In

addition, the FCD vehicle detection technique will be

used in order to detect different vehicle types. Finally,

a weighted traffic light schedule will be used to enable

truck signal priority.

II. Methods

This section describes the design of the Truck Signal

Priority (TkSP) controller and a cast study to evaluate

the performance. The work of this research is based

on the predictive controller DIRECTOR developed by

Van Senden [15]. The control algorithm is modified

and extended with the ability to make a schedule

decision based on different vehicle types. In this

research, the main focus is the truck signal priority.

For this reason, only cars and trucks will be part of

the study.

Prioritization of specific vehicle types requires the

signalized intersection controller to detect different

vehicle types. Literature described multiple detection

techniques, which are able to detect different vehicle

types. Especially, floating car data showed potential.

In addition, floating car data has the possibility to

send additional information, such as direction, speed

and vehicle type. Therefore, the method used to

detect an approaching truck is based on floating car

data. The range of the detection is set to a distance

of 330 m, based on the minimum distance between

the intersection under control and an upstream

intersection in the network. Alternatively, the selected

distance could also be extended to a larger distance,

which enables earlier knowledge of an approaching

truck. However, an increased detection distance

would increase the uncertainty of the estimated time

3

of arrival. If a larger detection distance is required, a

truck could update its floating car data more

frequently to reduces the uncertainty of the estimated

time of arrival. For simplification is the floating car

data of the truck limited to one update throughout

this research. Accordingly, the estimated time of

arrival is calculated, which is based on the distance to

the intersection and a speed limit of 50 km/h.

Resulting in an estimated time of arrival of

approximately 24 seconds. For this reason, three 10

second time bins are proposed, the index of the time

bin is denoted as T. Figure 1 illustrates the point of

detection and the corresponding time bins in an

approach of the intersections.

Figure 1 - Point of detection

It should be noted that the count for the time bins

starts at zero on the stop line of an intersection and

increases in steps of one moving upstream. The truck

arrivals, denoted 𝜏, will be assigned to a time bin. Eq.

(1) gives the result of the combined truck arrivals of

a single Origin-Destination.

According to the truck arrival time bins, discussed in

the previous section, a set of priority weights is

proposed. This set will correspond to the three first

time bins, since the trucks have an estimated arriving

time at the intersection within 30 seconds. In the

future this set could be extended to more time bins.

However, it should be noted that if more time bins

are used the uncertainty would increase as well. The

set of priority weight will be denoted as 𝜔. Table 1

illustrates the index corresponding to the time bin.

Table 1 - Priority weight index to corresponding time bin

𝜔 [0] 𝜔 [1] 𝜔 [2]

Time [s] (0,10) (11,20) (21,30)

The assigned weights are based on the differences in

vehicle characteristics. Suthaputchakun and Sun [3]

make a connection between different types of

vehicles based on their actual weight. For example,

heavily loaded vehicles normally have higher

emissions and consume more fuel. For this reason,

Suthaputchakun and Sun assumed that the weight of

a heavily loaded vehicle is two-times higher than that

of the small vehicles. However, more vehicle

characteristics are found in the literature. For

instance, the length of a truck is around 1.5 to 4 times

the length of a standard car [16]. Further, significant

differences are found in the vehicle dynamics,

especially in the acceleration rates after a complete

stop. A typical truck has an acceleration rate around

five times lower compared to a passenger car, when

accelerating to 50 km/h [17]. Combining the vehicle

characteristics above, an assumption is made on the

impact of a truck at an intersection compared to a

standard car. The following characteristics are taken

into consideration to determine the impact: weight

(2), length (4) and acceleration rate (5). The values

correspond to the number of times a truck has more

impact compared to a standard car. However, not all

defined vehicle characteristics are taken as equal. It

is assumed that the acceleration rate of a truck has

two times more impact on the intersection. Since, all

cars stopped behind a truck are limited to the

acceleration rate of the truck in front. The weighted

average of the vehicle characteristics above result

that a truck has four times more impact at the

intersection compared to a standard car. Hence, an

extra weight of four is given to an arriving truck at a

specific time bin. A truck is required to anticipate on

red light earlier than a regular car, due to their slow

dynamics. Therefore, it is assumed that a truck will

experience delays, if the signal is not green in the ten

seconds before a truck arrives. The extra weight will

be added to the second time bin to ensure a smooth

passage for a truck crossing the intersection.

Consequently, the set of priority weights is given in

eq. (2).

𝜔[𝑇] = [1, 4, 1] (2)

However, queues could already exist in front of an

arriving truck. In the same way as stopping for a red

light, a truck has to anticipate earlier on a queue. To

account for the possible queues and clear them

before a truck is arriving, a second weight is added in

the set of priority weights. However, this second

priority weight should not be equal to the value of the

second time bin. Since, it is intended to clear a queue

in advance and not give early green. The value of the

additional weight is therefore proposed to be half of

the value of the second time bin. It follows that the

priority weight is added in the third time bin, because

this is the farthest time bin away from the

intersection. Finally, the proposed set of priority

weights is found in eq. (3).

𝜔[𝑇] = [1, 4, 2] (3)

The next step is to calculate the truck priority weight,

denoted as 𝛿. The basic idea is to multiply the truck

arrival by the assigned priority weight, given by eq.

(4).

𝛿 = 𝜏 ∗ 𝜔 (4)

𝜏[𝑇] = [0, 0, 1] (1)

4

However, one or more approaches of the intersection

could have multiple Origin-Destinations. For this

reason, it is required to receive the Origin-Destination

from the floating car data of the truck. In addition,

multiple trucks could approach the intersection on the

same Origin-Destination. To find the truck weight of

one Origin-Destination, the weight of the approaching

trucks should be summed. Accordingly, eq. (4) is

rewritten to add the weights of three time bins

together. The calculation of the truck priority weight

of a single Origin-Destination is described by eq. (5).

𝛿𝑂𝐷[T] = ∑𝜏[𝑇] ∗ 𝜔[𝑇]

2

𝑇=0

(5)

However, multiple Origin-Destinations are often not-

conflicting and can be serviced simultaneously. The

set of combined Origin-Destinations is described as a

Phase Group. To find the truck weight of a Phase

Group, denoted as 𝛿𝑃𝐺, all Origin-Destinations within

the Phase Group are summed up following eq. (6).

𝛿𝑃𝐺[T] = ∑ ∑𝜏[𝑇] ∗ 𝜔[𝑇]

2

𝑇=0𝑂𝐷∈𝑃𝐺

= ∑ 𝛿𝑂𝐷[T]

𝑂𝐷∈𝑃𝐺

(6)

Finally, the schedule decision including the priority

weights for approaching trucks could be described by

eq. (7).

𝜒[𝑇 + 1] =


{

∑ 𝜌𝑂𝐷[𝑇 + 1]

𝑂𝐷∈𝑃𝐺

− σ𝑃𝐺[𝑇 + 1]

+ 𝛿𝑃𝐺[𝑇 + 1] }


{

∑ 𝜌𝑂𝐷[𝑇 + 1]

𝑂𝐷∈𝑃𝐺

− σ𝑃𝐺[𝑇 + 1]

+ ∑ 𝛿𝑂𝐷[𝑇 + 1]

𝑂𝐷∈𝑃𝐺 }


{( ∑ 𝜌𝑂𝐷[𝑇 + 1] + 𝛿𝑂𝐷[𝑇 + 1]

𝑂𝐷∈𝑃𝐺

)

− σ𝑃𝐺[𝑇 + 1]

}

(7)

Case study The proposed controller is evaluated within a case

study. The simulation software PTV Vissim is used to

develop and evaluate the case study. The controller

algorithm is written in Python and connected via a

COM (Component Object Model) interface to Vissim.

The case study will use an intersection that is located

in the Netherlands near Hoofddorp in the province of

Noord-Holland. The Vissim model for the intersection

of the case study is available and the historic data is

received form the province of Noord-Holland, which

is provided in a V-Log data format. A format in which

Dutch traffic light controllers log their data [15]. The

V-Log data only records changes in the data stream,

which contains the detectors states, signals states

and the internal system state of the traffic light

controller. The historic data used in the simulation

dates from January 2017 to May 2017 and is compiled

from the V-Log format to numerical data that can be

used for machine learning and simulation purposes

[15]. The data compiled from the V-Log data does not

contain information on the vehicle type. Therefore, it

is required to create a realistic data set of truck

arrivals. The truck arrival data is estimated from the

original detector data. This data contains the

timestamps of when a detector becomes occupied

and when the detector is free again. These

timestamps are compared to find the occupation time

of a detector. In this occupied time data is a three-

point median search conducted to find expected truck

detections. If the median is equal or larger than nine

seconds, then the point is marked as a truck

detection. Figure 2 shows the truck detections,

marked with red dots, for a single Origin-Destination

in 24 hours. These truck detections are saved to a

new data file, in the same structure as the original

detections, to use as an input for the truck

simulations.

Figure 2 - Truck detections (marked with red dots)

The PTV Vissim model for the intersection of the case

study is available and provided by the province of

Noord-Holland. However, this model contains a larger

network of multiple intersections, which are not used

in this research. Although they are not actively used

in the simulation, they use computational power of

the computer to simulate the vehicles in the network.

Therefore, all unnecessary elements in the PTV

Vissim network are removed to have an optimal

network for a smooth simulation. Subsequently, the

model is adjusted to be able to replay historical data

of vehicle arrivals. The locations of the vehicle inputs

are shown in Figure 3. Further, multiple vehicle

detectors are used in the model. These are displayed

as blue rectangles in Figure 3. During the simulation

the controller reads the states of the detectors every

5

100 milliseconds. The change between a state of the

detector is used to count vehicles passing a detector.

As each lane has two detectors, i.e. the arrival

detector and stop line detector, the controller can

count arriving and departing vehicles. Combining

information of two detectors on the same lane

enables the controller to calculate current queue

length of each Origin-Destination pair. Further, the

controller is able to change the state of the signal

heads in PTV Vissim, which are shown as red stripe

in Figure 3. Due to limitations of the simulation

software the changes are limited to once every

second. Subsequently, measurement points are

added for each individual Origin-Destination, in order

to evaluate the controller on the following outputs:

the number of stops per vehicle and the vehicle delay.

The measurements points are vehicle travel time

detectors in PTV Vissim. This detector type measures

the time it takes a vehicle to travel from one point to

the next. The locations of the travel time detectors

are displayed in Figure 3, where the pink line is the

start of the measurement and the green line is the

end of the measurement. The following outputs can

be calculated with these measurements: the vehicle

travel delay, the number of stops and the number of

vehicles passing. A delay of a vehicle is calculated

when the actual travel time is compared to the travel

time it would need under free flow conditions. Free

flow conditions are considered as for example when

the vehicle can maintain its desired speed, i.e.

without reacting on another vehicle or a red signal. In

addition, each individual Origin-Destination of the

measurement could be specified for the different

vehicle types. Hence, evaluations could be reviewed

for three configurations: all vehicles, only cars and

only trucks

Figure 3 - Vissim network layout of the intersection

III. Results

To evaluate the performance of the new design for

truck signal priority, simulations following the case

study of the previous section are conducted. These

simulations include two sets of different weights for

trucks. The first, is the baseline of [1, 1, 1]. In this

configuration no extra weight is added to a truck in

the schedule decision. The second, is the proposed

set of [1, 4, 2]. In this configuration, extra weight is

added to a truck in the schedule decision. Here, a

truck is equal to four cars in the second time bin and

equal to two cars in the third time bin. Both

configurations are simulated with a data set of 24

hours and have the same vehicle inputs. The

simulations are evaluated according the Key

Performance Indicators (KPI): the number of stops

and the vehicle delay (s). The results showed that the

simulation with priority weights, compared to the

baseline, has 87 stops less for trucks and 265 stops

more for cars. Further, the total vehicle delay for

trucks is reduced by 45 minutes (2673 seconds) and

increased by 111 minutes (6642 seconds) for cars.

Given the above, the introduction of priority weights

for trucks has a positive influence on the key

performance indicators for the trucks. However, it has

a minor negative impact for the cars.

To analyse the sensitivity of the priority weights a

Design of Experiments (DoE) is proposed. This

method aims to describe the results under a variation

of conditions. By introducing one change in the set of

priority weights it is expected to result in a change in

one or more output variables. Following, the priority

weights are chosen as input variables. The output

variables contain the key performance indicators: the

number of stops and vehicle delay. The set of priority

weights will start at [1,1,1], which is the baseline. In

this configuration no extra weight is added to a truck

in the schedule decision. Subsequently, the variables

are changed one by one. The first time bin is always

one, as described in the previous section. The other

two will vary in a range of eight values, starting a one.

The used weight data set is illustrated in Table 2.

Table 2 - Weight data set

The overall objective is to reduce the number of stops

for trucks. However, the implementation of priority

weights should have a minimal impact on the traffic

flow of other vehicles. Multiple weight configurations

show a decrease in the vehicle delay, compared to

the baseline for both cars and trucks. In addition,

three weight configurations also show a reduction in

6

the number of stops. Especially the weight

configuration [1, 3, 4]. This is also noticed Table 3

and Table 4, which present the total stops and vehicle

delay for three weight configurations: the baseline,

the first proposed and the weight configuration

[1, 3, 4]. The results showed that the weight

configuration [1, 3, 4] also outperforms the baseline

for the other vehicle groups. The new weight

configuration reduced the total stops by 183 for cars

and 143 for trucks. Further, the total vehicle delay for

cars is decreased by 59 minutes (3558 seconds) and

54 minutes (3253 seconds) for trucks. However, the

simulations are limited to 24 hours and one set of

vehicle inputs.

Table 3 – Total stops: Sensitivity analysis

Total stops

All Car Truck

[1, 1, 1] 23239 22750 489

[1, 4, 2] 23417 23015 402

[1, 3, 4] 22911 22567 344

Table 4 - Total vehicle delay: Sensitivity analysis


All Car Truck

[1, 1, 1] 653197 637582 15615

[1, 4, 2] 657166 644224 12942

[1, 3, 4] 646386 634024 12362

Different sets of vehicle inputs should be evaluated to

check the robustness of the weight configuration [1,

3, 4]. Therefore, six additional days have been

simulated, each having a different set of vehicle

inputs of 24 hours. Subsequently, the results could be

evaluated for a complete week, as can be seen in

Figure 4. The number of vehicles over a week are

presented in Table 7. The overall objective is similar

to the previous analysis, which quickly reduces the

number of suitable weight configurations. In fact,

only one weight configuration meets the objective, [1,

2, 1]. Compared to the baseline it reduces the total

stops by 77 for cars and 88 for trucks, and decreases

the total vehicle delays for cars by 61 minutes (3673

seconds) and 66 minutes (3957 seconds) for trucks.

Although, the weight configuration [1, 2, 1] meets the

objective, the results are not impressive. Since it only

reduced the total stops for trucks by 88 over a week.

However, the weight configuration [1, 3, 4] shows a

more significant reduction in the total number of

stops for trucks over a week, while the increase in the

total number of stops for cars remains minimal. The

comparison can be found in Table 5 and Table 6.

Table 5 – Total stops: Week

Total stops

All Car Truck

[1, 1, 1] 142225 139404 2821

[1, 3, 4] 141629 139559 2070

Table 6 - Total vehicle delay: Week


All Car Truck

[1, 1, 1] 3987494 3897966 89528

[1, 3, 4] 3981301 3909999 71301

Table 7 - Number of vehicles: Week

Origin-Destination Total 2 3 4 6 7 8

All 81954 30138 35030 41641 44135 73159 306057

Cars 81405 29582 34007 40300 43781 72499 301574

Trucks 549 556 1023 1341 354 660 4483

Finally, it can be concluded that the weight

configuration [1, 3, 4] reduces the total of stops for

trucks by 751 over a week, compared to the baseline

[1, 1, 1]. However, the total stops for cars increases

by 155. Further, the total vehicle delay over a week

shows a similar trend as the total stops. Namely, the

total vehicle delay for trucks is decreased by 304

minutes (18227 seconds) and increased by 201

minutes (12033 seconds) for cars. Given the above,

it is noticed that both the total stops and the total

vehicle delay decreases more for trucks than it

increases for cars. As a result, the priority weight

configuration [1, 3, 4] showed an overall

improvement of the key performance indicators over

a week.

Figure 4 – Sensitivity analysis: Week

7

IV. Discussion

The expected results for adding priority weights are

confirmed by the results. Compared to the baseline

[1, 1, 1], the weight configuration [1, 4, 2] shows a

decrease in stops and vehicle delay for trucks.

However, cars experience a minor increase in the

total stops and the vehicle delay. Therefore, the

overall performance is negatively impacted in terms

of total stops and total vehicle delay. This can be

explained because there are more cars in relation to

trucks in the simulation. To find if it is possible to

reduce the number of stops and vehicle delay for

trucks, without negatively impacting other vehicles,

more priority weight configurations should be

evaluated.

By performing a design of experiments to analyse the

sensitivity of the priority weights, it was expected to

find a relation between the priority weights

configuration and the key performance indicators.

The higher weight configurations, which reduces the

number of stops for trucks compared to the baseline.

On the other hand, the number of stops for cars are

increasing for higher weight configurations. Similar

results are found for the vehicle delay. It follows that

the higher priority weight configurations do not have

the desired results, since they negatively impact the

traffic flow.

Another interesting result is noticed, it showed that it

is more beneficial to have a higher value for the third

time bin in combination with a lower value for the

second time bin. This finding indicates the importance

to clear a possible queue in front of the arriving truck.

In order to cross the intersection without a stop.

However, the weight values 6, 7 and 8 in the third

time bin also show a major increase in the totals stops

and total vehicle delay for cars. This may indicate that

the trucks are simply given too much priority weight

before they arrive at the intersection. On the other

hand, it could also indicate that a weight is added to

clear a queue, while no queue is present. A possible

solution would be to first check the current queue and

only add the priority weights if a queue is present.

Different sets of vehicle inputs were evaluated to

check the robustness of the weight configuration

[1, 3, 4]. Subsequently, the results could be

evaluated for a complete week. The results showed

one priority weight configuration that met the

objective, which was [1, 2, 1]. However, the results

were not impressive. Since it only reduced the total

stops for trucks by 88 over a week. On the other

hand, the weight configuration [1, 3, 4] showed a

more significant reduction in the total number of

stops for trucks over a week, while the increase in the

total number of stops for cars remained minimal.

Subsequently, the priority weight configuration

[1, 3, 4] was evaluated for the individual days. The

evaluation showed that the performance of the

priority weight configuration, in terms of total stops

and vehicle delay, is depended on the traffic demand.

This indicates that is would be beneficial to have an

dynamic priority weight configuration, which would

adapt to the current traffic demand. Another

approach could be to implement of multiple priority

weight configurations for each Origin-Destination.

The priority weight configurations could then be

optimized per Origin-Destination, since the traffic

demand is often different for each Origin-Destination.

However, the results for the priority weight

configuration [1, 3, 4] showed an overall

improvement of the key performance indicators over

a week. Despite, increasing the total stops and total

vehicle delay for cars. It can be concluded that the

proposed truck signal priority controller design can

reduce the number of stops for trucks at a signalized

intersection, while maintaining the overall traffic flow

at least as good as a state-of-the-art model predictive

intersection controller.

V. Conclusion

The results of this research showed that the

implementation of priority weights for trucks has an

influence on the performance of the intersection

controller, in terms of number of stops and vehicle

delay. Early results presented a comparison of a

proposed priority weight configuration to a baseline

with no priority weights. Simulating a full day, it was

found that the simulation with priority weights has 87

stops less for trucks and 265 stops more for cars

(17,8% and 1,16% respectively). Further, the total

vehicle delay for trucks is reduced by 45 minutes and

increased by 111 minutes for cars (17,1% and 1,0%

respectively). A sensitivity analysis was performed to

further study the effects of the priority weights. The

results showed that the priority weight configuration

[1, 3, 4] outperforms the baseline. The total stops

were reduced by 143 for trucks and 183 for cars

(29,67% and 0,8% respectively), also the total

vehicle delay for truck was decreased by 54 minutes

and 59 minutes for cars (0,6% and 20,8%

respectively). Subsequently, a robustness check was

performed to evaluate the performance of the priority

weight configuration over a week. The results showed

an reduction of the total number of stops by 751 and

total vehicle delay by 304 minutes for trucks over a

week (26,6% and 20,4% respectively). While, the

8

total number of stops and total vehicle delay for cars

increased, by 155 stops and 201 minutes (0,1% and

0,3% respectively). However, the overall total

number of stops and total vehicle delay were reduced

by 596 stops and 103 minutes (0,42% and 0,16%

respectively). It can be concluded that the proposed

truck signal priority controller design can reduce the

number of stops for trucks at a signalized

intersection, while maintaining the overall traffic flow

at least as good as a state-of-the-art model predictive

intersection controller.

References

[1] Nunzio, G. D., “Trafc eco-management in urban trafc

networks,” Université Grenoble Alpes, 2015.

[2] Kaparias, I., Zavitsas, K. & Bell, M.G.H., “State-Of-

The-Art of Urban Traffic Management Policies and

Technologies,” CONDUITS, Coordination Of Network

Descriptors for Urban Intelligent Transport Systems,

Imperial College London, 2010.

[3] Suthaputchakun, C. & Sun, Z., “A Novel Traffic Light

Scheduling Based on TLVC and Vehicles’ Priority for

Reducing Fuel Consumption and CO2 Emission,” IEEE

Systems Journal, vol. 12, no. 2, pp. 1230-1238, 2018.

[4] Wu, L., Yusheng, C., Chu, J. & Zhang, H., “The

Influence of Intersections on Fuel Consumption in

Urban Arterial Road Traffic: A Single Vehicle Test in

Harbin,” PLoS One, China, 2015.

[5] Brilon, W. & Zurlinden, H.,

“Ueberlastungswahrscheinlichkeiten und

Verkehrsleistung als Bemessungskriterium fuer

Strassenverkehrsanlagen (Breakdown Probability and

Traffic Efficiency as Design Criteria for Freeways),”

Forschung Strassenbau und

Strassenverkehrstechnik, Heft 870, 2003.

[6] Hochreiter, S. & Schmidhuber, J., “Long short-term

memory,” Neural computation, vol. 9, pp. 1735-1780,

1997.

[7] Lämmer, S., Donner, R. & Helbing, D., “Anticipative

control of switched queueing systems,” The

European Physical Journal B, vol. 3, no. 63, p. 341,

2008.

[8] Kumar, P.R. & Seidman, T.I., “Dynamic instabilities

and stabilization methods in distributed real-time

scheduling of manufacturing systems,” IEEE

Transactions on Automatic Control, vol. 3, no. 35, p.

289–298, 1990.

[9] Lämmer, S. & Helbing, D., “Self-stabilizing

decentralized signal control of realistic, saturated

network traffic,” Santa Fe Institute Working Paper,

Santa Fe, 2010.

[10] Lämmer, S. & Helbing, D., “Self-control of traffic

lights and vehicle flows in urban road networks,”

Journal of Statistical Mechanics: Theory and

Experiment, no. 04, 2008.

[11] Tiedong, W. & Jinging, H., “Applying Floating Car

Data in Traffic Monitoring,” 2014 IEEE International

Conference on Control Science and Systems

Engineering, Yantai, China, 2014.

[12] Fabritiis, C. de, Ragona, R. & Valenti, G., “Traffic

Estimation And Prediction Based On Real Time

Floating Car Data,” in Proceedings of the 11th

International IEEE, Conference on Intelligent

Transportation Systems, Beijing, China, 2008.

[13] Fedra, K., Greppin, H., Haurie, A., Hussy, C., Dao, H.

& Kanala, R., “GENIE: An integrated environmental

information and decision support system for Geneva.

Part I: Air quality,” Archives des Sciences, vol. vol.

49, pp. p. 247-263, 1996.

[14] Banach, S., “Betrouwbaarheid en toepassingen van

Floating Car Data,” Technische Universiteit

Eindhoven (TUe), Design & Decision Support Systems

(DDSS), Eindhoven, 2013.

[15] van Senden, J.C. , “DIRECTOR: Enabling advanced

driver assistance systems with predictive signalized

intersection control using LSTM networks,” Delft

University of Technology, Faculty of Electrical

Engineering, Mathematics and Computer Science,

Embedded Software Section, Delft, The Netherlands,

2018.

[16] Garber, N.J. & Hoel, L.A., Traffic & Highway

Engineering, 4th edition, Cengage Learning, ch. 3,

2008.

[17] Institute of Transportation Engineers, Traffic

Engineering Handbook, Washington DC: 5th Edition,

ch. 5, 2000.

73

B B. Python modules and

packages

74 B. Python modules and packages

Python version:

- Python 3.6.5 :: Anaconda, Inc.

Spyder version:

- Spyder 3.3.1

The following modules and packages are used for the simulations in this research:

- sys

- pickle

- time

- datetime

- json

- os

- queue

- math

- win32com.client

- numpy

o Version: 1.14.3

o Summary: NumPy: array processing for numbers, strings, records, and objects.

- openpyxl

o Version: 2.5.3

o Summary: A Python library to read/write Excel 2010 xlsx/xlsm files

o Home-page: https://openpyxl.readthedocs.io

- pandas

o Version: 0.23.0

o Summary: Powerful data structures for data analysis, time series, and statistics

o Home-page: http://pandas.pydata.org

- XlsxWriter

o Version: 1.0.4

o Summary: A Python module for creating Excel XLSX files.

o Home-page: https://github.com/jmcnamara/XlsxWriter

75

C C. Vehicle arrivals

76 C. Vehicle arrivals

Table 18 - Car arrivals

77

Table 19 - Truck arrivals

79

D D. Simulation results

80 D. Simulation results

Figure 39 – Week

Figure 40 – Tuesday

81

Figure 41 – Wednesday

Figure 42 - Thursday

82 D. Simulation results

Figure 43 - Friday

Figure 44 - Saturday

83

Figure 45 - Sunday

Figure 46 - Monday

85

E E. Simulation data

86 E. Simulation data

Table 20 - Simulation data: Week number of stops

87

Table 21 - Simulation data: Week vehicle delay (s)


Table 22 - Simulation data: Tuesday number of stops

89

Table 23 - Simulation data: Tuesday vehicle delay (s)


Table 24 - Simulation data: Wednesday number of stops

91

Table 25 - Simulation data: Wednesday vehicle delay (s)


Table 26 - Simulation data: Thursday number of stops

93

Table 27 - Simulation data: Thursday vehicle delay (s)


Table 28 - Simulation data: Friday number of stops

95

Table 29 - Simulation data: Friday vehicle delay (s)


Table 30 - Simulation data: Saturday number of stops

97

Table 31 - Simulation data: Saturday vehicle delay (s)


Table 32 - Simulation data: Sunday number of stops

99

Table 33 - Simulation data: Sunday vehicle delay (s)


Table 34 - Simulation data: Monday number of stops

101

Table 35 - Simulation data: Monday vehicle delay (s)

103

F F. Emission data

104 F. Emission data

Table 36 - Emission data Tuesday Wednesday Thursday Friday Saturday Sunday Monday Week

[1,

1,

1]

CO [g] 49916 49513 47737 39848 33568 39152 48962 308696

NOx [g] 9712 9634 9288 7753 6531 7618 9526 60061

VOC [g] 11568 11475 11064 9235 7780 9074 11347 71543

Fuel consumption [gal] 714 708 683 570 480 560 700 4416

[1,

4,

2]

CO [g] 50147 49528 47676 39898 33609 39353 49271 309483

NOx [g] 9757 9636 9276 7763 6539 7657 9586 60214

VOC [g] 11622 11479 11049 9247 7789 9120 11419 71726


[1,

3,

4]

CO [g] 49409 49496 47515 39905 33568 39045 48871 307808

NOx [g] 9613 9630 9245 7764 6531 7597 9508 59888

VOC [g] 11451 11471 11012 9248 7780 9049 11326 71337


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