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816 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 50, NO. 3, MAY 2001

IDUTC: An Intelligent Decision-Making System forUrban Traffic-Control Applications

M. Patel and N. Ranganathan

AbstractThe design of systems for intelligent control ofurban traffic is important in providing a safe environment forpedestrians and motorists. Artificial neural networks (ANNs)(learning systems) and expert systems (knowledge-based systems)have been extensively explored as approaches for decision making.While the ANNs compute decisions by learning from successfullysolved examples, the expert systems rely on a knowledge basedeveloped by human reasoning for decision making. It is possibleto integrate the learning abilities of an ANN and the knowl-edge-based decision-making ability of the expert system. Thispaper presents a real-time intelligent decision making system,IDUTC, for urban traffic control applications. The system inte-grates a backpropagation-based ANN that can learn and adaptto the dynamically changing environment and a fuzzy expert

system for decision making. The performance of the proposedintelligent decision-making system is evaluated by mapping thethe adaptable traffic light control problem. The application isimplemented using the ANN approach, the FES approach, andthe proposed integrated system approach. The results of extensivesimulations using the three approaches indicate that the integratedsystem provides better performance and leads to a more efficientimplementation than the other two approaches.

Index TermsArtificial neural network, expert system, integra-tion, intelligent vehicle highway system (IVHS), urban traffic con-trol (UTC).

I. INTRODUCTION

POPULATION growth has increased the number of vehi-cles and passengers on the countrys freeways and high-ways. Since the current transportation infrastructure has not keptpace with the growth in traffic demand, research to develop

modern transportation systems has become important. With in-

adequate space and funds for the construction of new roads, and

the growing imbalance between traffic demand and transporta-

tion resources, intelligent highway vehicle systems (IVHSs) are

gaining interest. The study of IVHS solutions has evoked sub-

stantial interest in Europe, Japan, and the United States. An

IVHS system would perform tasks that are typically done by

human operators and use advanced technologies from various

fields such as image processing, computer vision, intelligent

controls, and artificial intelligence (AI). The IVHS systems areclassified into four categories:

1) advanced traffic management system (ATMS);

2) advanced driver information system (ADIS);

3) freight and fleet control system (FFCS);

4) automated vehicle control system (AVCS).

Manuscript received November 1, 1997; revised October 1, 2000.M. Patel is with the Honeywell Space Systems Commercial Systems Divi-

sion, Clearwater, FL 33764 USA.N. Ranganathan is with the Computer Science and Engineering Department,

University of South Florida, Tampa, FL 33620 USA.Publisher Item Identifier S 0018-9545(01)03954-8.

The ATMS systems perform tasks such as surveillance, control,

and management of freeway and arterial networks. Such appli-

cations include intersection traffic light control and congestion

and incident management. The ADIS systems are responsible

for such tasks as origindestination calculation and motorist ad-

visories. They provide information such as efficient alternatives

forreaching destinations based on time and road conditions. The

FFCS systems manage cargo and freight traffic. Since cargo

and freight vehicles are massive in number and size, efficient

systems are required to ease fuel consumption, congestion, and

road wear and tear caused by such transports. The AVCS sys-

tems include vehicle platooning, obstacle avoidance, and au-

tonomous vehicle guidance. The system concentrates on intel-ligent guidance systems for vehicles. They are capable of either

driving the vehicle in a fully automatic manner or giving the

human driver useful advice. Such systems would allow drivers

to operate at high speeds and simultaneously reduce the proba-

bility of having accidents or collisions.

A. Intelligent Decision Making Systems

Artificial Intelligence techniques used in IVHS systems in-

clude artificial neural networks (ANNs) [7], [3], expert systems

[23], [2], [27], andfuzzy logic controllers [29], [24], [5], [3], [4],

[15], [26]. There are two common approaches for intelligent de-

cisionmaking:one based on learning systems, such as theANNs,and the other based on expert systems. In a learning system, the

decisions are computed using the accumulated experience or

knowledge fromsuccessfully solved examples. The learningsys-

temsuse variousmethodsand mathematicalmodelsto exploitthe

computational power of a computer, regardless of the inferencing

power of humans. ANNs can be used to compute solutions for

complex problems. They possess an adaptive feature that allows

each cell within the network to modify its state in response to

experience. The neural network can then learn or self-modify.

Often, ANNs have been used to mimic expert systems. In an

expert system, problems are solved using a computer model of

human reasoning. Various implementations of expert systems

canbefoundintheliterature.Severalsystemarchitecturesforim-plementing neural networksand a fewschemesfor implementing

expert systems exist in the literature. The problem of integrating

a neuralnetwork and a fuzzy expert system and its application to

urban traffic control is themain focus of this work.

An intelligentdecision-making system must1) be ableto solve

problems of practical nature and size, 2) always arrive at correct

solutions, and 3) adapt to the changes in the application environ-

ment. The typical human is constantly faced with makingimpor-

tantdecisions and almost always uses prior knowledge or experi-

ence in determining them. Experts have accumulated knowledge

00189545/01$10.00 2001 IEEE
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PATEL AND RANGANATHAN: IDUTC: INTELLIGENT DECISION-MAKING SYSTEM 817

over the years within their respective fields. They are able to for-

mulate accurate decisions and explain and support their conclu-

sions. However, the experts are not always available or reliable.

Thus, a system that can learn without much human intervention

and make intelligent decisions is useful.

In this paper, we propose a new real-time intelligent decision-

making system for urban traffic-control applications that is im-

plementable in hardware. The proposed system, called IDUTC,integrates an artificial neural network that can learn and adapt in

a dynamically changing environment, and a fuzzy expertsystem

for decision making. The performance of the proposed intelli-

gent decision-making system is evaluated by mapping the adapt-

able traffic light control problems. The application is imple-

mented using the ANN approach, the FES approach, and the

proposed integrated system approach. The results of extensive

simulations using the three approaches indicate that the inte-

grated system provides better performance decisions for urban

traffic control and leads to a more efficient implementation than

the other two approaches.

The outline of this paper is as follows. In Section II, the

background and motivations that influence intelligent decisionmaking are discussed. Section III describes the architecture of

the proposed system. The section discusses the various compo-

nents: 1) fuzzification unit, 2) artificial neural network, 3) fuzzy

expert system, and 4) defuzzification unit. Section IV describes

the mapping of the application to the proposed system. Some

concluding remarks are given in Section V.

II. BACKGROUND AND MOTIVATION

The design of an efficient intelligent decision making system

depends on 1) knowledge acquisition and scope and 2) decision

explanation. During knowledge acquisition, the knowledge base

of an expert system is formed with the aid of an expert inter-acting with a knowledge engineer. The knowledge acquisition

process consists of the following subtasks [1]:

1) knowledge extraction;

2) formal representation of knowledge;

3) coding;

4) validation.

The development of the knowledge base starts with the knowl-

edge engineers extracting and formalizing the data acquired

from an expert. The process of extracting knowledge may be

constrained by a number of problems, such as:

1) nonavailability of an experienced knowledge engineer;

2) unwillingness of the expert to share knowledge;3) inconsistency among the experts;

4) nonuniform representation of the experts knowledge;

5) time constraints of the expert.

Since the quality of knowledge representation affects the effi-

ciency, speed, and maintenance of the system, the method of

knowledge representation is critical. The choice is usually lim-

ited by theapplicationdomain, thepreferencesof theknowledge

engineer, and the expert.

In learning systems such as ANNs, the knowledge acquisition

task is performed by the training process. However, the training

process, in most cases, is a time-consuming task requiring the

application of input training patterns in an iterative manner. The

process is constrained by various parameters that guide the be-

havior of the system such as 1) the type, size, and definition of

the training data; 2) the learning rate; and 3) the topology of

the ANN. The process of training may also be constrained by

the uncertainty as to whether the final learning goal has been

achieved.

Knowledge scope is an important factor that determines the

range of situations that the intelligent system can manage. Anintelligent system must be able to handle unexpected situations

using the existing knowledge. Expert systems have a limited

scope [8] and operate in narrow domains under restricted condi-

tions. Decisions canonly be made forsituations withinthe scope

of the knowledge. On the contrary, learning systems such as an

ANN operate under dynamically varying conditions. Thus, the

ANNs can perform decision making using adaptable decision

boundaries. The decision boundaries created during the training

process can adapt to unexpected situations including inconsis-

tent input data.

An important aspect in intelligent system design is decision

explanation, which involves supplying a coherent explanation

of its decisions [25]. This is required for 1) acceptability of thesolution and 2) correctness of the reasoning. The expert systems

can explain the reasoning process by evaluating the trace gener-

ated by the inferenceengine or by analyzingthe rule base (which

typically use IF THEN rules). Also, in learning systems such as

ANNs, knowledge is represented in the form of weighted con-

nections, making decision tracing or extraction difficult.

Thus, using an ANN or an expert system approach to intelli-

gent decision making leads to different levels of performance

depending on the model as well as the application. By inte-

grating the two approaches, it is possible to overcome the de-

ficiencies associated with using a single approach.

III. IDUTC: INTELLIGENT DECISION-MAKING SYSTEM

IDUTC is a real-time intelligent decision-making system that

computes decisions within a dynamically changing application

environment. The architecture consists of an artificial neural

network and a fuzzy-rule-based expert system. The architec-

tural blocks and the data flow for the proposed IDUTC system

are shown in Fig. 1. Sensors are used to detect the surrounding

environmental conditions in this model. The sensors send crisp

data inputs to the artificial neural network. The fuzzification unit

assigns fuzzy labels to the outputs of the ANN. These labels in-

dicate the degree to which each crisp value is a member of a

domain. Then, the fuzzy expert system fires the rules based onthese fuzzy values. The defuzzification unit converts the com-

puted decisions into crisp values that are used to control the en-

vironment within an application.

The proposed intelligent decision making system requires an

artificial neural networkmodel that can handle a wide range

of applications and is simple in terms of implementation and

training [22]. Based on the existing research [16], the backprop-

agation model has been found to be the most suitable choice.

The ANN model used in this work is a fully connected, single

hidden layer backpropagation network. The model is used in

many applications ranging from speech synthesis to loan appli-

cation scoring.
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Fig. 2. Fuzzy expert system data flow.

centroid (center of gravity) for the output membership func-

tion is given by [19]

(9)

where is the index of the horizontal axis of the membership

function . The center of gravity equation is discretized to

form an equivalent equation that is implementable in hardware.

The simplified equation can be realized with basic logic blocks.

The defuzzified output is approximated by the weighted average

(10)

where is the number of elements along the horizontal axis of

the membership function .

A. Functional Interaction Between the ANN and the FES

In IDUTC, the fuzzy expert system computes a decision by

firing one or more rules from the rule base. In the decision-

making process, the antecedents of a rule are processed with the

inputs to the FES using the max-min composition rule, where a

rule is represented as an IF-THEN statement as shown below

if then

Here, is the input to the FES, is the rule antecedent, andis the rule consequent. Then (7) becomes

(11)

for the th rule, is the input to the FES in the range

is the antecedent, is the consequent,

and is thecomputed decision.The FES firesrules based

on the output of the ANN. For a rule to be fired, the input has

to completely or partially match the rule antecedent, as shown

in Fig. 3. A value of one indicates a complete match, while a

zero corresponds to a mismatch. In partial matches, there is an

imprecision in the match, which is reflected in the membership

value. In the rule base, the response of each rule is weighted ac-

cording to the confidence or degree of membership of its inputs.When there are multiple antecedents (antecedent indexed by

), a rule ( ) is fired based on the weighted combinations of the

antecedents and the inputs to the FES, as shown in the equation

below

(12)

(13)

Here, is the number of antecedents of in rule indexed by

is the th input to the rule , and is the membershipfunction of the rule consequent. The operator indicates the

min operation of elements, is the decision of rule with

antecedents, and is the combined decision of rules.

The artificial neural network provides bias values based on

the decision surface. Each neuron in the ANN forms a hyper-

plane, and a set of hyperplanes form a decision surface. The

neuron outputs are based on the weighted combination of the

interconnection lines and the input patterns as in the forward

neural network equations equations [10]

(14)

(15)

Here, is the result of the multiply-accumulate operations be-

tween the weights and the inputs is the number of

fan-in connections to node , and is the sigmoid activation

function of node . The value, ranging between zero and one,

is calculated when the input patterns are applied to the ANN.

This value is represented as a point on the decision surface.

In IDUTC, the ANN provides the inputs to the fuzzy expert

system. Therefore, (11) becomes

(16)
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Fig. 3. Matching comparison.

The computed FES decision is shown to be biased by

the ANN output, which is assigned a fuzzy label . Since

the ANN outputs are obtained from the different weighted con-

nection lines, is the bias value that depends on the decision

surface after learning. Thus, the rule firing sequence is dynam-ically determined by the values of obtained from the knowl-

edge in the ANN.

An application can be modeled in different ways, as shown in

Fig. 4. In the figure, and are the sizes of the rule base, is

the output of the ANN, isthe fuzzy labeled ANN output,

and is the fuzzy labeled input from the environment.

is the weight of the th rule and is the weighted combination of

each rule. In the integrated system, the ANN models the dynam-

ically changing environment, which provides input to the FES

for decision making as shown in Fig. 4(b). The s are applied to

the rules, and only the matched rules are fired. Thus, IDUTC

is an integrated system that models a dynamically changing en-

vironment and adaptively makes decisions.

In an FES system, as shown in Fig. 4(a), the model is gener-

ated once using fixed parameters based on the existing knowl-

edge. But for new events or occurrences of events with low prob-

ability, the static system would require additional rules. Also,

due to the larger rule base, the fixed firing sequence could in-

ference without using the most appropriate rule(s). On the con-

trary, by modeling a dynamic environment with an ANN, the

size of the FES rule base is reduced, and the rule firing se-

quence changes with the environment. In addition, there is a

higher chance that the ANN will fire the correct rule(s) due its

smaller rule base. In the design of IDUTC, the way the rules are

fired and the data flow between the ANN and the FES play animportant role in mapping a specific application to the architec-

ture.

As an example of the integrated system, the adaptive traffic

light application is shown in Fig. 5. The environment provides

an input weight vector that represents the current traffic con-

ditions. A vector consists of the highest saturation (HS) and

the cross-saturation (CS) values, the saturation difference (SD),

the volume difference (VD), and the required green time ex-

tension (RG). The weight vector represents the predicted

next traffic conditions provided by the ANN. This is used to fire

dynamically the selected rules from the rule base. The output

vector represents the new traffic light cycle values. It con-

sists of values for the cycle time (CT), the green time (GT), and

the green time extension (GText) for adjacent intersections. The

rules used for decision making consist of five antecedents and

three consequents in this example.

In Fig. 5(b), the traffic environment is modeled as a static

systemusing an FES consisting of rules. Forthe staticsystem,

the input values from the traffic sensors are directly fed into the

FES. In Fig. 5(a), the traffic environment is modeled as a dy-namic system using an ANN and an FES. Both systems consist

of different rules used to adjust the traffic light cycle times. The

model in Fig. 5(b) uses an ANN, which provides a traffic predic-

tion vector that acts as weights or bias for selecting rules in

the FES rule base. Since the rule base in the integrated system is

smaller , it is more probable that the right set of rules

will be fired for cycle adjustment.

IV. TRAFFIC LIGHT SYSTEMS

The traffic light control problem is important task in the in-

telligent vehicle highway system (IVHS). Traffic light control is

used to resolve conflicts among the movement of vehicles andpedestrians at junctions. The objective is the reduction of the

confusion generated by the interaction of different users and,

consequently, the improvement of the safety and the relief of the

discomfort suffered by both drivers and pedestrians [6], [12].

Most of the currently implemented traffic control systems are

grouped into two principal classes: 1) fixed-time systems and

2) vehicle actuated systems [13]. In the first group, the green

times for the streams are implemented regardless of the current

arrival of vehicles and pedestrians. In the vehicle actuated op-

eration, the change of the signal is influenced by the prevailing

traffic flow.

The control systems can be further divided into four basic

forms: 1) pretimed, 2) semi-actuated, 3) fully actuated, and 4)fully actuated with volume-density control. The methods range

from fixing the green time cycles to altering the green cycles

time based on current traffic patterns. In addition to the different

systems, there are a number of different implementations of the

systems. They include 1) off-line optimization, on-line imple-

mentation; 2) on-line optimization, stepwise steady-state flow

conditions; and 3) on-line optimization, response control. The

implementations range from solely preselecting signal plans

based on historical data to a more predictive selection of plans

based on some optimizing criteria. The off-line approaches do

not implement real-time conditions, and the on-line approaches

suffer from problems such as 1) poor quality in traffic predic-

tions, 2) ill-chosen traffic indexes, and 3) inability to deal with

occasional events [13].

In this section, the mapping of the adaptive traffic light con-

trol problem to the proposed IDUTC system is investigated.

First, the related work on the traffic light system is discussed,

followed by the details of the feasibility and the mapping. Fi-

nally, the simulation results and the performance comparisons

with the ANN and FES approaches are discussed.

A. Traffic Light Systems: Related Work

Several approaches and designs have been described for

the traffic light control problem. The works include fuzzy
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(a)

(b)

Fig. 4. Modeling a dynamic environment.

(a)

(b)

Fig. 5. Two models for traffic light control.

controllers [4], an adaptive controller [15], and regional system

controllers such as TRANSYT [18], [26], SCOOT [18], [26],

SCAT [18], [26], ATSAC [11], and ARTC [17]. In the work by

Pappis and Mamdani [4], fuzzy logic was applied to controlan intersection of two one-way streets. Sensors are placed up-

stream to detect vehicles that approach the intersection. From

the sensor data, the number of vehicles and queue lengths are

evaluated by a fixed set of fuzzy rules to determine whether to

extend the current cycle time by or more seconds. The design

is static and is not adaptable to the dynamically changing traffic

patterns. The design by Favilla [15] is similar to that by Pappis

and Mamdani. Fuzzy rules are used to control cycle times, but

the membership functions that represent the linguistic variables

are adaptive. Also, the degree of adaptability is bounded,

and the changes in membership do not cover other traffic

conditions. Additionally, the controller design does not include

coordination of adjacent or cross intersection traffic, which

would ease the average number of delays and stops.

The off-line system TRANSYT computes signal timing plans

[18]. The plans are computed based on the geometry of the

traffic network and the average behavior of the traffic on each

approach. Since the system is based on a macro model of the

traffic network, the cycle and split times are updated based on

the average delays and stops of the traffic. Because the average

values are used, actual traffic flows are not considered. The

SCOOT and SCAT systems respond to changing traffic demands

by performing incremental optimizations at the regional level.

The SCOOT [9] controller oversees a collection of traffic con-trollers. It periodically calculates incremental adjustments for

cycle, phase, and offset times in 4-s variations. SCOOT changes

timing parameters in fixed increments to optimize an explicit

performance objective. The regional controller calculates ad-

justments for each intersection based on the data provided by

the signal controllers and the cyclic flow profile (CFP). The

CFPs are used to estimate queues that develop at the intersec-

tion. Every 4 s, the CFP uses average traffic flow values to create

patterns that represent the flows at each approach, where the

CFP represents realistic traffic patterns using delay time and

number of stops as a performance index. The main objective of

SCOOT is to minimize the queue lengths to extend phase times

within a cycle in response to sudden traffic changes.The SCATS system [18], [17] is similar to the SCOOT in

that a collection of traffic controllers are managed by a regional

controller using a common cycle length. SCATS computes the

cycle, the split, and the offset times using degrees of saturation,

where the saturation value indicates how well a particular in-

tersection is being used. The adjustments are calculated off-line

based on the traffic flow of the intersection. These values are

sent to a controller, and adjustments are made to the intersec-

tion.

The Los Angeles Automated Traffic Surveillance and Con-

trol (ATSAC) [11] uses a hierarchical setup where a regional

controller oversees several traffic controllers. Several traffic de-
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Fig. 6. Adaptable traffic light control system.

tectors are used to collect data involving 1) intersection control,

2) real-time surveillance, and 3) real-time evaluation and auto-mated generation of traffic signals. In ATSAC, there are four

options used to control signal plans:

1) time-of-day;

2) critical intersection control (modifies green time at the

intersection);

3) traffic flow;

4) manual override by technician.

The Areawide Real-Time Traffic Control (ARTC) system

controls a collection of traffic light intersections by providing

control based on current data from detectors [17]. Signal

controllers are used to store data that reflect the history of the

traffic flow and are used to determine arrival patterns. Thedata are used to form CFPs that estimate the sizes of traffic

platoons and their locations. Based on the CFP, the system

provides sufficient green cycle times for an approach such that

the queue formed at an intersection is diminished. Approaching

cars that are added to the end of the queue are also considered.

The system computes cycle times indirectly, by computing a

split that reflects the current queue lengths at the approach. At

the end of the split, another split for the crossing approach is

computed or the current split is extended, where the sum of the

splits is the total cycle length. The system also allows progres-

sion of traffic such that higher level traffic from neighboring

signal controllers can pass without stopping.

Most of the described systems control traffic at a regionallevel overseeing several traffic controllers. However, such

systems that control large areas may not be responsive to

changes that occur at individual traffic lights. By requiring the

regional traffic controller to control large areas, a breakdown

would cause all the intersections to fail or default to a fixed

timing plan. Since the techniques for cycle adjustments rely on

traffic flow coming into an intersection, some systems do not

consider traffic flow on adjacent approaches. Also, the system

response times may be constrained by 1) the computational

cost associated with the cycle calculations for each intersection

and 2) the distance the signals travel from the main regional

controller.

B. Adaptive Traffic Light Application Feasibility

The adaptive traffic light problem requires a system that can

predict the traffic flow through a lighted intersection and sug-

gest an appropriate light cycle-time adjustments to control the

traffic light. The cycle times should be adjusted such that the av-

erage vehicle wait times are minimized. The environment of the

traffic intersection consists of dynamically changing traffic pat-

terns throughout the 24-h day. The traffic flow is characterized

by two parameters: volume (vehicles per hour) and occupancy

(percent of the hour that the detector was occupied). In the inte-

gratedsystem, while theANN canbe used to model thebehavior

of the traffic, the FES can be used to deterministically select

the set of appropriate cycle-time adjustment rules based on the

outputs of the ANN. The integrated system offers the ability tohandle any new intersection traffic conditions and provide ex-

plainable actions to ease the traffic.

The ANN in the IDUTC system models the dynamic traffic

environment. The ANN can predict the traffic flow for a given

time of day based on the traffic flow through an intersection

of the present and the previous time frames. A time frame is

typically 12 min. In our experiments, the ANN was trained

on the traffic flow values from the previous day and tested on

traffic inputs from the current day. The FES would receive the

predicted traffic flow and compute the cycle-time adjustment

value.

C. Mapping of the Adaptive Traffic Light Application onto

IDUTC

This section describes the mapping of the adaptive traffic

light control application onto the IDUTC system architecture.

The adaptive traffic light system is shown in Fig. 6. The system

uses two closed-loop detectors placed on each of the four

upstream approaches (NorthSouth, SouthNorth, EastWest,

WestEast) of an intersection, shown in Fig. 8, where each

approach has two lanes. The closed-loop detectors return two

traffic parameters, which describe the traffic flowing through

the intersection. The traffic parameters are volume (vehicles

per hour) and occupancy (percent of the hour that the detector
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was occupied); however, only the volume parameter is used

by the IDUTC system [26]. Using the volume data, five traffic

parameters are computed that describe in more detail the

NorthSouth (SouthNorth) and EastWest (WestEast) traffic

flow through the intersection. The five parameters are:

1) the highest saturation;

2) the cross saturation;

3) the saturation difference of the traffic;4) the volume difference;

5) the required green time extension [26].

The four parameters from the previous time frame (the satu-

ration difference was not included) and the five parameters for

the current time frame are inputs to the ANN in IDUTC, where

a time frame rangesfrom 1 to2 min [13]. The previous value for

saturation difference parameter was not chosen as input to the

ANN. Through extensive analysis of the test data set, the satura-

tion difference parameterwas found to be constant between time

frames, and thus only the current time frame value was required.

As a preprocessing step, the previous four parameters and the

five computed parameters are stored and normalized to between

zero and one relative to the largest volume value, being 500

(number of vehicles/hour). The ANN acting as a predictor out-

puts five values, which correspond to the five predicted traffic

parameters for the next time frame. Each output of the ANN is

a value between zero and one. The fuzzification process assigns

a fuzzy label to each output of the ANN, and the labels are then

sent to the FES. The FES receives the five inputs and computes

three decisions indicating how much to adjust the cycle time,

green time, and green time extensions at the intersection.

The five traffic parameters are used to decide the degree to

which the cycle, green, and green extension times are adjusted.

The cycle times are adjusted for the approach with the greatest

amount of traffic. The controller at the intersection determinesthe dominant direction from the vehicle counts. First, the cycle

time is adjusted to maintain a good degree of saturation on the

intersection approach with the highest saturation. The degree of

saturation for a given approach is the actual number of vehicles

that passed through the intersection during the green period di-

vided by the maximum number of vehicles that can pass through

the intersection during that period. The degree of saturation is a

measure of how effectively the green period is being used. The

reason for adjusting the cycle time is to maintain a given degree

of saturation to ensureefficient use of the green periods and con-

trol delay and stops. When traffic volume is low, the cycle time

must be reduced to maintain a given degree of saturation, re-

sulting in short cycle times that reduce delays in waiting. Whenthe traffic volume is high, the cycle time must be increased to

maintain the same degree of saturation, resulting in long cycle

times and reducing the number of stops. The rules that govern

the cycle-time adjustment use as inputs the highest degree of

saturation (the highest degree on any approach) and the cross

saturation (the degree on the competing approach.)

Second, the green cycle time is influenced by the phase split

adjustment. The phase split adjustments are used to maintain

equal degrees of saturation on competing approaches. The rules

that govern the green time extension use as inputs 1) the differ-

ence between the highest and cross saturation values and 2) the

highest saturation value.

Finally, the green time extension is used to coordinate the ad-

jacent signals in a way that optimizes stops in the direction of

dominant (greatest) traffic flow. Based on the next green time

of the intersection, the arrival time of a vehicle group leaving

the upstream intersection can be calculated. If a local signal

becomes green at the same time, then the vehicles will pass

through the local intersection unstopped. The green extension

of the next phase change is calculated based on this target greentime. The rules that govern the green time extension use as in-

puts 1) the volume difference (the normalized difference be-

tween the traffic volume in the dominant direction and the av-

erage volume in the remaining directions) and 2) the required

adjustment (the amount by which the current green phase is to

be ended early divided by the current green period).

For the intersection, four previous and five current parameter

inputs are used to predict the next five traffic parameters. In the

experiments, an ANN configuration with nine input units and

five output units was used. After the nine input values are ap-

plied to the ANN, the ANN outputs five values, which corre-

spond to the the values for the next time frame. The ANN out-

puts values, ranging from zero to one, areconverted into integersbetween zero and 15 (by multiplying by 15). The corresponding

integers are used when assigning fuzzy labels to the ANN out-

puts. The value of 15 corresponds to the maximum value of the

-axis used for the membership functions shown in Fig. 7. The

bound from zero to 15 was chosen to reduce the amount of hard-

ware required to represent a membership function. The decimal

to integer conversion is required so that the integer value can be

used to address the RAM, which stores the membership func-

tions. The fuzzy labels assigned are based on the two linguistic

variables, shown in Fig. 7(a). The figure shows two member-

ship function with seven and five linguistic variables, respec-

tively, which are used to label the five traffic parameters. Both

the input and the output membership functions shown in Fig. 7

were obtained from [26], where a fuzzy expert system was used

to control the traffic cycle times.

The FES in IDUTC applies the five fuzzified inputs to the

antecedents in the rule base. An arrangement of a typical rule

consists of five antecedents and three consequents, as shown

below

If

then CC GC AA

Here, the antecedents Hsat, Crsat, SatDiff, Voldiff, and

RGrnExtIS represent the highest saturation, cross satura-tion, saturation difference, volume difference, and required

green time extension predicted by the ANN, respectively,

and represent the input antecedents from the

rule. The rule antecedents correspond to the five

parameter values (the possible state of the intersection) that

must either match or partially match the inputs to the FES

in order for the

rule to be fired. The three consequents are the CycleAdj(CC),

GrnAdj(GC), and AGrnAdj(AA) and represent the cycle-time

adjustment, green time adjustment, and allowed green time

extension, respectively. The fuzzy linguistic variables for the

FES decisions are shown in Fig. 7(b).
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(a)

(b)Fig. 7. Traffic light linguistic variables: (a) input and (b) output.

An sample subset of fuzzy rules for the traffic light appli-

cation is shown in Table I. The table is organized as follows:

columns 1 to 5 correspond to the fuzzy labels for each of the

five traffic parameters, and columns 6 to 8 are the fuzzy labels

for the three cycle-time adjustments. The first row of Table I

shows that the dominant approach Hisat(the approach with the

greatest amount of traffic) is highly saturated, while the other

cross approach Crsat is low in saturation. Thus the cycle time

should be adjusted by an amount corresponding to pbig, which

helps the traffic on the dominant approach and reduces the time

for the cross approach.

The training and the test data sets for the adaptive traffic light

control application were provided by the Orange County De-

partment of Transportation [28]. The traffic light intersection of

Sand Lake and Turkey Lake Roads, Orlando, FL, was targeted,

where the closed-loop traffic detectors [28] at the intersection

are numbered from one to eight. The detectors numbered 1 and2 are for westbound traffic, 5 and 6 are eastbound, 3 and 4 are

southbound, and 7 and 8 are northbound traffic. The data sets

were collected from November 11, 1995, to January 4, 1996,

over the weekdays and weekends, 24 h a day. Each intersection

approach has two lanes with two closed-loop detectors. The

closed-loop detectors observe the approaching traffic and

collect the volume data (vehicles per hour). The data were

collected every hour (60 min) and interpolated to minutes using

Matlab, beginning from minute 0 (midnight) to minute 1440.

From the interpolated data, the five traffic parameters for the

intersection were calculated. For the simulation, the closed-loop

detector outputs were averaged over the number of lanes for

the NorthSouth/SouthNorth and the EastWest/WestEastapproaches, producing two values where one is the dominant

approach value and the other is the cross approach value.

The dominant (greatest) and the cross traffic flow parameters

for the day of November 12, 1995, were plotted in Fig. 9. It was

observed that the traffic volume was low in the morning and in-

creased to a peak volume, then decreased to almost zero for the

late hours. By using the relationship between the traffic volume

and the time of day, the cycle-time adjustments are computed

based on the results of the ANN, which predicts the traffic pa-

rameters for the next time frame. For the simulations, the ANN

received as input the flow parameters of the previous time frame

(2 min in the past) and the current time frame and predicted the

traffic parameters for next time frame (next 2 min in the future).

The ANN was trained on the data from a single days (24 h)

traffic flow for predicting the next days traffic flow.

1) ANN and FES Design and Simulation: The closed-loop

detectors provide traffic volume information of the intersec-

tion to the IDUTC system. As a preprocessing step, the IDUTC

system converts this information into the five traffic parameters.

The four previous (stored in a memory device) and five cur-

rent traffic parameters traffic parameters are sent to the IDUTC

system. Based on the data, the ANN in IDUTC predicts the

traffic parameters for the next time frame, and then the FES in

IDUTC receives the predicted values and computes cycle-time

adjustment decisions.A neural network simulator was written similar to the

one for the obstacle avoidance application. The simulator

used computations using 16-bit fixed-point precision and can

simulate ANNs of different configurations with an arbitrary

number of nodes and hidden layers. The experiments reported

here use a fully connected backpropagation network with

a sigmoid activation function (output range 0.0 to 1.0). A

network with nine inputs (one input each for the previous

and the current highest and cross saturations, the saturation

difference, the previous and the current volume differences, and

the previous and current required adjustments), one bias input,

one hidden layer with 40 hidden nodes, and five output nodes
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TABLE IA SAMPLE SUBSET OF FUZZY RULES FOR ADAPTIVE TRAFFIC LIGHT SYSTEM

Fig. 8. Sand LakeTurkey Lake intersection, Orlando, FL.

(one output each for the highest and the cross saturations, the

saturation difference, the volume difference, and the required

green time adjustments for the next time frame) for each was

empirically found to give the best performance. The previous

and current required adjustments were synthetically generated

values. The the output nodes produce values between zero and

one, and the values correspond to the normalized predicted

traffic parameters.

In order to train and test the ANN in the proposed IDUTC

system, training and test data sets, referred to as -train and

-test, respectively, were obtained from the from Sand Lake and

Turkey Lake intersection in Orlando, FL, shown in Fig. 8 over

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Fig. 9. Plot of time versus volume.

the three days of November 11, 13, and 14, 1995, sampled every

2 min. The traffic flow data was obtained for an additional 19

days to further train the ANN. For each day, 720 patterns were

obtained representing a 24-h day. A single training and test set

was used, where the training set consists of 720 patterns from

the day of November 11, and the test set consists of 1440 pattern

from the days of November 13 and 14. Each pattern consists of

nine input values (four previous and five current traffic parame-

ters) and five desired outputs (next time frame parameters). The

desired outputs were created using a program written in that

computes a desired output per pattern by solving the equationgenerated by Matlab for the plot in Fig. 9 given the time and the

traffic parameters.

Fournetworkswith identical configurations withdifferent ini-

tial random weights were trained using the -train data set. As a

method of choosing the right ANN, four ANNs were arbitrarily

chosen and trained, and the best performing ANN was used for

further experiments. During the training, each actual output of

the ANN per pattern was compared to the desired output. If the

training e rror actual output desired output , t he

ANN learned thepattern. The error limit was chosen because the

output of the ANN is assigned a fuzzy label based on the fuzzy

membership function,and theresolution of thehorizontal axis of

the m embership f unction i s .Thus, it i s necessarythat the error value distinguish between adjacent ele-

ments.Thetrainingof theANNwascontinueduntil thesumofthe

squares of t he training e rror was less than 0.1. I f

theerrordoes notfallbelow 0.1, theANN is allowed to train until

3000 epochs. The large number of epochs was chosen to give the

ANN sufficient time to converge if possible.

When the trained ANNs were tested using the -test data

set, the best ANN yielded an average prediction rate of 98%.

This means that for the test data set, the best performing ANN

predicted the five next traffic parameters correctly 98% of the

time. The training time on the SUN Sparc 20 was 4 h of CPU

cycle time on the average.

The fuzzy expert system for IDUTC was designed with five

inputs, three outputs, and a rule base of 40 rules. Each rule con-

sists of five antecedents (one for each of the five traffic param-

eters) and three consequents (one for each cycle-time adjust-

ment). The rule base of 40 rules was obtained from the fuzzy

expert implementation of traffic light control in [26].

2) Simulation Results: The adaptive traffic light application

was mapped onto the architecture and simulated in using

a simulator similar to the obstacle avoidance application. The

IDUTC system was formed by combining the best performing

ANN (98% average classification rate) and the FES. The testdata used to evaluate IDUTC is referred to as -test, which con-

sists of 1440 patterns, where each pattern contains nine inputs

and five desired outputs. During the simulation, the patterns are

input to the ANN in IDUTC. After the pattern has been applied,

IDUTC computes a decision for a total of 1440 decisions using

the -test data set. After each decision is computed, the deci-

sion is labeled as a correct or an incorrect cycle-time adjustment

decision.

In order to evaluate the outputs of the FES in IDUTC, a pro-

gram was written that labels the decisions as a correct or an in-

correct cycle-time adjustment. The program uses the traffic pat-

terns from the -test data set to label each FES decision. The

average vehicle wait time is also computed based on the FES de-cision. The average vehicle wait time is the duration for which

the vehicle is stopped at an intersection before passing through.

The vehicle wait times are estimated with respect to the domi-

nant flow of traffic. When a cycle time is adjusted, the vehicles

on the cross approach incur a wait time equal to the new cycle

time for the dominant approach. By accumulating the cycle-time

adjustments over the test patterns, the average wait times are es-

timated for the day. An initial cycle time of 3 min was assumed

for the computations.

When the IDUTC system was evaluated using the -test data

set, the system computed the correct cycle-time adjustment

amount 95% of the time and incorrect actions 5% of the time.
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TABLE IIINPUTOUTPUT PAIRS FOR ADAPTIVE TRAFFIC LIGHT CONTROL

Thus, the system computed decisions that decrease the wait

times 95% of the time. The average wait time for the cross

approach was computed to be 2.186 min on the average.

A sample input/output combination for the IDUTC system

is shown in Table II. In the table, columns 1 to 5 correspond

to the normalized predicted outputs of the ANN for each of

the traffic parameters and columns 6 to 8 correspond to the

crisp cycle-time adjustment computed by the FES. The adaptive

traffic control is illustrated by examining Table II. For example,

in the second row of the table, the normalized values for thefive traffic parameters for the next time frame are 0.55, 0.21,

0.65, 0.00, and 0.00, respectively. Each value is multiplied by

15 in order to index the fuzzy labels from the membership func-

tion shown in Fig. 7(a). For example, the normalized value for

Hisat(0.55) is multiplied by 15, resulting in 8.25, which is then

assigned a fuzzy label of good. The same operations are per-

formed for each of the five parameters, and the fuzzy labels are

then sent to the FES. After all the data have been processed by

the ANN, the three fuzzy decisions are computed and defuzzi-

fied into three crisp decisions (the centroids of the three decision

membership functions), shown in row 2. The crisp values corre-

spond to the adjustment amounts to the current cycle time, the

green time, and the green time extension used at the intersec-

tion. The decisions indicate that the cycle time does not need

to be slightly adjusted, but the green time should be adjusted to

allow additional traffic to flow on the cross route.

D. Artificial Neural Network Approach

The adaptive traffic light problem was modeled using the

ANN approach. The ANN model included predicting the

traffic parameters for the next time frame and computing the

cycle-time adjustment values. A network with nine inputs (one

for each past and present traffic parameter), one bias input,

one hidden layer with 70 hidden nodes, and three output nodeswas empirically found to give the best performance. The three

values produced by the ANN output nodes range between

[0 1] and indicate the required adjustments required to the

cycle times to help traffic flow through an intersection.

The training and the test data sets used for the IDUTC system

simulations were used for the ANN approach (referred to as

-train and -test, respectively). The -train and -test data

sets consist of 720 and 1440 patterns, respectively, where each

pattern contains nine sensor inputs and three desired outputs.

The nine inputs per pattern in the -train and the -test data sets

correspond to the nine sensor inputs per pattern in the -train

and -test data sets. The three desired outputs were computed

using the simulation results of the IDUTC system (when tested

on the -train and -test data sets) and the program, which

labels the decisions of the FES within the IDUTC system.

The ANN after training was evaluated on the -test data set.

The testing of theANN over theentire setof test patterns yielded

an average evaluation rate of 73%, which means that the ANN

predicted the three cycle adjustments to ease the intersection

traffic 73% of the time. The average wait time was computed

to be 2.958 min on average. An additional experiment was con-

ducted using 32-bit floating-point precision rather than 16-bitfixed-point precision. The ANN was then trained and tested on

the -train and -test data sets, yielding an average evaluation

rate of 75.3%. The average wait time was computed to be 2.78

min on the average. The outputs of the ANN were analyzed to

determine the cause of the average performance for both the

16-bit fixed- and the 32-bit floating-point precision. The results

indicate that the ANN had learned the training data, but during

the testing, the ANN had difficulty in generalizing on the var-

ious numbers and the combinations of traffic parameters and

required cycle-time adjustments (desired outputs).

E. Fuzzy Expert System Approach

Next, the adaptive traffic light problem was modeled using

the fuzzy expert system approach. In the FES model, a rule base

is used to compute the cycle-time adjustments. The simulation

code and the fuzzy variables used for the FES in the IDUTC

system were also used in the FES approach. However, only five

inputs (five traffic parameters for the current time frame) were

used because the FES approach does not predict the next traffic

parameters. If the FES approach was used as a predictor, more

rules will be required to accommodate the various combinations

of antecedents, each producing a decision. Thus, it was decided

to restrict the FES approach to only compute decisions on cur-

rent time-frame traffic inputs. Thus, the FES model required fiveinputs, three outputs, and a rule base of 40 rules. Each rule has

five antecedents and three consequents.

The test data set used for the IDUTC system simulation was

rearranged and used for the FES approach. The test data for the

FES approach consists of the 1440 traffic input patterns, where

each pattern contains five current traffic input parameters. When

the FES was tested on the 1440 patterns, the approach yielded

a performance rate of 95%. This means that given a set of test

patterns, the FES determined cycle-time adjustment to ease the

intersection traffic 95% of the time. The decisions also resulted

in the vehicles at the cross approachs waiting 2.975 min on the

average.

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TABLE IIICOMPARATIVE PERFORMANCE SUMMARY

F. Results and Comparisons

A summary of the simulations for the IDUTC system, the

ANN, and the FES approaches is given in Table III. The results

show that the IDUTC system provided decisions that relieve in-

tersection congestion better than the ANN approach and was

comparable to the FES approach. The results also show that the

IDUTC system imposed a lower average vehicle wait time than

the other two approaches. The ANN approach required more

neural nodes than for the ANN in IDUTC, which led to slower

training and a higher implementation cost. The FES approachcomputed correct decisions well; however, the computed deci-

sion did not lead to a better reduction in the wait times. The FES

approach computed decisions based on the current traffic flows

only. Additional rules would be required to compute decision

using both previous and current values, as in in IDUTC.

The simulation summary indicates that the IDUTC system

provided better decisions at a lower cost compared to the other

approaches. The summary also shows that the IDUTC system

provided more effective cycle-time adjustments. As more func-

tions are added to the adaptive traffic light problem, the number

of rules and the ANN nodes required considerable increases,

making theimplementation of the ANN and the FES approaches

more complex. In the IDUTC system, the integration of the twomodels helps in reducing the number of nodes in the ANN and

the number of rules in the FES, leading to a better and compact

implementation.

V. CONCLUSION

A real-time intelligent decision-making system called

IDUTC has been presented. The system consists of a backprop-

agation-based ANN that can learn and adapt to a dynamically

changing environment and a fuzzy expert system for decision

making. It was shown that the integrated system performs

better than the ANN and the fuzzy expert system approaches.The evaluations were performed by mapping the adaptable

traffic light problem onto the proposed IDUTC system. The

performance of the proposed intelligent decision-making

architecture was evaluated using real data. The results indicate

that the IDUTC system provides better performance at a lower

implementation cost compared to the other two approaches.

The IDUTC system was also evaluated by mapping two other

IVHS applications. The applications were the autonomous

vehicle obstacle avoidance and the freeway congestion detec-

tion and recovery in urban traffic control. In the autonomous

vehicle problem, the ANN is used to model the behavior of

the obstacles and the target vehicle, and the FES is used to

deterministically select a set of appropriate avoidance rules. In

the congestion detection problem, the ANN is used to model

the behavior of the freeway traffic and the target vehicle, and

the FES is used to deterministically select a set of appropriate

detour rules. The details and results of the additional mappings

can be found in [21].

Since the integrated system improves efficiency and

decreases complexity, the limitations of individual implemen-

tations are overcome. The objective of this paper has been to

demonstrate the potential of integrating ANNs, expert systems,

and fuzzy logic for improving the effectiveness of processingand control of a variety of traffic problems. The effectiveness

of such a designs capabilities was demonstrated by mapping

the traffic light control problem onto the system. Currently,

a VLSI implementation of the system is being implemented.

Since a VLSI design provides a low cost, high performance,

and compact chip design, several such chips can be placed at

urban traffic locations. It is envisioned that strategically placed

chips could eliminate much of the conventional hardware, such

as typical traffic light stations.

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