Group Proposal on Dynamic Public Transit System

8/3/2019 Group Proposal on Dynamic Public Transit System

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Imperial College London Electrical and Electronic Engineering

Concept Paper on Dynamic Public Transportation System

A DYNAMIC PUBLIC TRANSPORTATION SYSTEM

Intelligent Transportation System

Second Year Student Initiative

FEB 2012


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Content Page

1. Introduction2. Current Intelligent Public Transportation Systems

A. SingaporeB. ZurichC. London

3. Interview with Industry Player Cubic Corporation, Cubic Transportation System4. Proposed Model of Intelligent Public Transportation System

A. Informational Interfacei. Information Collection

1. Operating Environment2. Demand Level Assessment3. Resource Monitoring

B. Operation Interfacei.

Usage of Integrated Information

1. Government2. Transport Operators3. End-Users

C. System-wide Entityi. Integration of Collected Informationii. Distributive Information Structure

5. A Vision of Future Public Transportation System6. ConclusionI. Appendix A Congestion Facts and Figures

II. Appendix B Intelligent Transportation Systems

III. Appendix C 2019 Implementation Framework

IV. References


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Concept Paper on Dynamic Public Transportation System Page 1

1. INTRODUCTION

Many countries now face the challenge of mass transportation while having to deal with severe congestions within cities.

Increasing affluence has resulted in the extensive usage of private vehicles and this growth in the usage of private vehicles

is overwhelming the transport infrastructure. Carrying capacities of roads were not planned for todays number of

vehicles1

and yet, more private vehicles are still being added to the transport network. Public transportation networks

were not planned for the rapid increase in population and were often seen as the second-class mode of transport as

compared to private vehicles.

To cope with the transportation issues, conventional means such as building new transportation infrastructure were often

done but as long as the number of road users increase, the same problem will resurface. In addition, new infrastructure

such as highways often requires long project timespan and incurs huge costs when the development occurs within the

city. A new solution is urgently needed to deal with the growing demand for transportation.

In this research, the group aims to improve public transportation systems using informational resources in three primary

aspects, Operation Environment, Demand Level Assessment, and Resource Monitoring. The collected information can

then be processed by a System-Wide Entity that then proceeds to distribute the information to Transport Authority,

Transport Operators and Commuters.

2. CURRENT INTELLIGENT TRANSPORTATION SYSTEM (ITS)

There are many governments around the world that utilises information and communication technologies to increase the

efficiency of road networks. Examples of technologies that have been implemented include Dynamic Traffic Light, Road

Monitoring, Demand Modelling and Passenger Journey Planners that has been implemented as a mobile application. In

particular, countries such as Singapore and Switzerland have implemented an extensive range of ITS solutions to cope with

the increasing transportation demand.

A. Singapore Land Transport Authority (LTA)

In Singapore, the LTA runs an Intelligent Transport Systems Centre that monitors and operate several ITS solutions2. These

include the Green Link Determining System (GLIDE) which monitors and optimises green signals on roads, TrafficScan that

monitors road conditions, IBMs Symphony E-payment System that manages contactless payment on public transport and

many others.

Our interviews with a transport planner at LTA revealed that information generated by such systems has been used for

modelling as early as in the 1990s! Even though the information is only updated once a year, it has an accuracy of up to

90% for various stages proving the potential of transport modelling using travel data. Furthermore, the system has

helped the government in planning infrastructure improvements and to evaluate the impacts of improvements.

B. Switzerland, Zurich - ZVV

In Zurich, transport operators have implemented a Dynamic Traffic Signal Control which takes in real time traffic

conditions from different transport networks and the location of individual transit vehicles to establish the most optimal

phase and duration of traffic signals. Using the location of the transit vehicle, the system can predict the arrival time of

transit vehicles at road junctions up to an accuracy of 1 second.

1In 2008, there were 218,000 vehicles per km of road in Singapore. Refer to reference item 1

2Refer to reference item 11


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In addition, an interesting spill over effect of the system is that it also optimises transport networks for private vehicles as

the pipelining of vehicles in different road networks has allowed vehicles to utilise green signal time more effectively,

resulting in a smoother journey.

C. United Kingdom, London Transport for London (TFL)

In London, TFL has implemented ITS such as Automatic Vehicle Location into an overall bus traffic priority system known

as iBus. This system uses GPS to determine the position of the bus using virtual detectors and uses this information to

control traffic signals. However, such control procedures are individual events that do not relate to other junctions along

the transport network. To facilitate the flow of information, the bus locations are shared with the public on TFLs website.

TFL also monitors the usage of different public transport modes through conducting the London Travel Demand Survey3

in

where information on the journeys travelled by 8000 households is collected to better manage transportation demand.

3. INTERVIEW WITH INDUSTRY PLAYER Cubic Corporation, Cubic Transportation System

On the 9th

January 2012, our project group was given a rare opportunity to interview Cubic Transportation System at their

European Headquarters, which worked on the London Oyster Card Payment System. Cubic Transportation System handles

more than 1 billion passengers in a year and manages up to 50 million pounds of public transport revenue daily. The

interviewees were Mr. Matthew J. Cole, Sr. Vice President for Strategy and Business Development and Mr. Martin Howell,

Director for Worldwide Marketing and Communications.

Cubic Transportation System agrees that information generated by public transportation will be the next edge in

optimising public transportation systems. They are currently focusing on the transaction aspect of public transportation

where they aim to create a centralised payment system for each individual using the public transport. Such centralised

payment system could extend to cover various modes of transportation payments such as payment methods using mobile

devices, and account-based payment system that feedback travelling information back to passengers.

Currently, Oyster card readers on buses stores transaction data during the day and upload the transaction information to

the back office after the journey which readers at Tube stations update the database in a real-time basis. Consolidation of

data for TFL is then done overnight. However, Cubic is looking into implementing 3G readers on bus platforms to

incorporate a higher communication capacity.

As an industry player, they are unwilling to look at other forms of information collection on transport network as that is

not their companys strategy. However, they are interested in the information that can be collected from different public

transport subsystems.

4. PROPOSED MODEL OF INTELLIGENT PUBLIC TRANSPORTATION SYSTEM

Todays societies are becoming more instrumented, with nearly one billion transistors per human and over 30 billion radio

frequency identification tags produced globally. At the same time, the world is also becoming more interconnected with IP

traffic expected to exceed half a zettabyte in three years, 1021

bytes!4

In addition, with advanced analytics and

supercomputers, organisations and research institutes have been able to process information at resounding speed,

providing new insights in computational fields.

With such rapid technological developments, Mankind is now witnessing the confluence of three key technological drivers

the ability to generate significant amount of data, the means to transmit the data and the capability to process the vast

amount of data. Transportation as we know it, is about to change.




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ITS solutions that were implemented worldwide were initially implemented as stand-alone systems. Opportunities to

collect information from ITS platforms were lost and there is also no exchange of information between various systems. As

a result, transport authorities and operators are not achieving the full potential of ITS platforms.

Proposing a Truly Information-Integrated Public Transportation System

In order to utilise information for optimising public transportation, the proposed system is separated into two interfaces,

an Informational Interface for collecting information from sensors distributed across subsystems in the transportation

network and a Function Interface for distributing processed information to different end-users.

A. Information Interface

Under this interface, the system focuses on the role of data collection and refining the data for usage. To fully utilise the

collected data, information from different sources must be integrated for transport system administrators to get an

understanding of a system-wide health status of the transport infrastructure. Moreover, collected data will be interpreted

level-wise to reduce computational demands at data processing layers that are higher up in the informational hierarchy.

The three key layers in the proposed informational interface are the Raw Information Layer, Domain Layer and System

Layer(Information). In the Domain Layer, information is classified into three main categories Operating Environment,

Demand Level Assessment and Resource Monitoring.

SYSTEM-WIDE INTEGRATIONOF INFORMATION

Operating Environment

Physical Road/ Track Network andConditions

Scheduled External Events

Real-Time Traffic Status

Travel Profile of Other Vehicles

Demand LevelAssessment

Passenger Travel History

Crowd Density Levels

Event Monitoring

Primitive Location of User

Resource Monitoring

Vehicle Location and Status

On-board Passenger Count

Staff Deployment

Resource Planning

Raw Information Layer Domain Layer System Layer (Information)

Figure 1 : System Schematic Showing Information Interface of the Proposed Intelligent Public Transport System


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I. System Layer (Information)

The system layer is the system-wide informational entity that collates

processed information from each domain layer to integrate and analyse it to

provide ITS administrators with a situational awareness of the entire public

transportation system.

Using advanced analytics techniques and high-speed computational systems,

ITS administrators will be able to assess thecurrent health conditions of public

transportation, predict future changes to transportation systems, and react

promptly to any system failures with the appropriate contingent measures.

II. Domain Layer

The domain layer collates information in each of its three categories Operating Environment, Demand Level

Assessment and Resource Monitoring. Information in these three categories are collated and partially analysed in the

Domain Layer for critical, real-time information that requires immediate attention. This is to prevent information choke at

the system layer and also provide the general ITS with a certain level of redundancy.

III. Raw Information Layer

The raw information layer consists of distributed sensors that collect information in the different traffic and transportation

subsystems. Sensors are divided into the three categories to facilitate information flow with the Domain Layer. In this

section, the report will discuss about the various sensors that can be deployed or are already deployed. In addition,

technologies that are currently under development will also be discussed.

1. Operating Environment

Operating Environment relates to information pertaining to the external conditions surrounding the operation of public

transportation systems such as traffic conditions. The information collected will then reflect the constraints that the public

transportation systems operate under and allow transport authorities and operators to determine the appropriate limits

of operation for their resources.

Physical Road/Track Network and Conditions Information on road and track networks

have been actively collected and shared by transport authorities and operators5, it is

readily found online and in mobile applications. Transport authorities often manage

road and track networks through an operational centre or system such as the

Expressway Monitoring Advisory System used in Singapore. This is to facilitate prompt

action in a contingent event.

Scheduled External Events Planned events also meant that road availability are affected at times, for instance

road closures during New Year Countdowns. The collection of this information in the system allows operators to

mitigate the effects of such events on commuters.

Real-time Traffic Status Information about the level of congestion, average vehicle speed and traffic incident are

collected in real-time by transport authorities or companies. Current sensing techniques include speed monitoring

cameras, average vehicle travel time measured by induction loops between traffic junctions and GPS-equipped

vehicles6.



Figure 2: Operations Office of the Intelligent

Transport System Centre in Singapore


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In DevelopmentTravel Behaviour of All Other Vehicles on Road/Track Vehicle tracking technologies using

image recognition, built-in GPS systems or RFID tags provide the possibility of observing the behaviours of all

vehicles on the road network.

An on-going project is the eCall7. It is a European Commission scheme to equip all new vehicles with mobile

connectivity and GPS. 14 countries have signed up for this scheme and it is likely that other countries will sign up

as well. Under this scheme, location-based information of the vehicle can be collected and be transmitted via the

on-board mobile communication devices. This allows transport authorities to observe general behaviour of the

traffic users, to react to ad-hoc variations and predict future load on transport network.

2. Demand Level Assessment

Demand Level Assessment relates to information that pertains to the requested level of service from transport operators.

Variation in demand across different timings and locations can be monitored and predicted with this information. As a

result, with a clear understanding of passenger flow, transport operators will be able to distribute resources more

efficiently by providing higher service quality while minimising resource wastage.

Passenger Travel History Using the past records of a passengers journey ( i.e information such as

alighting/boarding time and location ), simulation models could predict transportation demands in a region and

transport operators will be able to plan the necessary transport service support levels to meet that demand. If

records are updated digitally in real-time, it is possible to get real-time predictions of passenger service demands.

Currently transport operators are trying to collect passenger past journey information by attracting commuters,

using incentives such as subsidised transport costs, to record down their journeys in a travel record card.

However, this method does not sample the entire commuter population and also does not reflect the ad-hoc

variation in demand levels.

A new technology that has been gaining momentum is the usage of contactless payment methods. Through the

contactless payment systems implemented by Transport for London and Land Transport Authority of Singapore,commuters travel records are stored real-time in digital databases for use in demand monitoring in statistical

models.

Having a record of travel history, transport operators will be able to charge flexible fare for transiting commuters

to appeal to a larger group of commuters and to reduce car ridership.

Currently, records are only examined periodically instead of a real-time basis. As a result, only routine trends such

as daily commuting between the school/work with home are captured in the system. However, unexpected

changes in demands are not met by corresponding changes in supplied transport resources resulting in resource

wastage. Passenger travel history should be examined in real-time in order to reflect any immediate change in

passenger commuting demands.

Crowd Density Level in Hot Regions Crowds sensors could be placed in crowd-prone areas to raise alerts when

crowd levels have rose to a certain threshold and thus the area is likely to require more transportation services

and support. In Singapore, there are cameras built to observe crowd levels in train platforms in order to estimate

the level of commuter demand at different train stations.

In crowded areas such as Oxford Circus, such crowd-based sensors could be integrated with existing security

cameras using image processing algorithms to track the level of crowd in separate timeframes. With such

information, transport systems are able to predict short-term changes in passenger commuting demand within

15 to 120 minutes.



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Event Monitoring Information about events happening across the city should be taken into account when

predicting demand level in a given area. For instance, the ending time of a rock concert or soccer match is a good

indicator that service demands in the region will peak rapidly. Moreover, demand variation can also be due to

long term events such as seasonal changes resulting in changing commuter profiles. (I.e varying percentage of

commuters who are tourists tend to peak during holiday periods)

This information about events across a country is often widely available in public. In addition, previous trends in

variation in transport demands have been recorded and investigated. This information can be used to predict

changes in demand levels over a long period of time.

Primitive Location of Users By knowing where passengers alight in real-time, transport operators can a rough

estimate on the level of crowd in an area and if the travel history of such commuters are available, this

information can be used to predict levels of passenger flow for return journeys. For instance, commuters from

Knightsbridge who alighted at South Kensington are more likely to stay for the next half an hour then commuters

who came from Marble Arch and may have also alighted at South Kensington for transit.

In DevelopmentUser Service Demand In the near future, mobile phones, digital security and widespread use

of mobile internet are likely to provide commuters with another form of payment method for public transit, suchas the NextVision system that is being planned by Cubic Corporation. Instead of waiting at the bus stop for their

buses, commuters could instead pay of pre-booked bus trips using their mobile phone ahead of the trip. For

instance, work-related trips could be pre-booked online with updates confirming their trip timings and in return,

transport operators will provide reliable estimated arrival timings and also estimated journey time information.

Pre-booking of trips has been implemented for long distance rails and flights due to high resource costs and low

passenger counts per route. This technology could be implemented for public transport should it be deemed

convenient enough for the general user and to be mutually beneficial to both commuters and transport

operators.

In DevelopmentLocation of Users In the near future, location-based information of users could be accessedvia voluntary public participation programmes or through location-based information collected as a by-product of

mobile communications. Knowing where a particular commuter might be will allow the system to better

understand the commuters behaviours and allow transport operators to plan their resources in order to match

demand better.

3. Resource Monitoring

Resource Monitoring relates to information about the resources managed by transport operators. The collection of this

information allows transport operators to plan transport operations more efficiently and to react rapidly to any sudden

fluctuations in the transport networks. In addition, this information should be made available to the System Layer for

transport authorities to understand the resource capacity of the transport network in order to deal with any contingent

situations and to plan future improvements in transportation.

Vehicle Location and Status Transport operators have implemented fleet management systems to monitor their

capability to deploy vehicles to take different scheduled passenger loads and also to provide contingency

measures during emergencies such as metro breakdowns.

Resource Planning Information that pertains to capacity resource planning by transport companies to deal with

future demands should be captured in the system to estimate the robustness of the transport system

On-board Passenger Count Information on the on-board passenger count provides transport operators an idea

of the load on their fleets, allowing them to adjust supply.


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Staff Deployment Other than knowing their physical resources, transport operators must also be aware of their

human resources. Vehicle captains and fleet support personnel are very crucial to the system as they cannot be

deployed without intermittent breaks or without sufficiently early notifications.

B) Operation Interface

In this interface, the system determines the optimal reactions to be taken by different ITS subsystems based on the

processed information from the informational interface. Decision making processes will be conducted in separate layers

such that each controller has autonomy over the subsystems that they control. In addition, this introduces certain degree

of redundancy to the system as subsystems that are critical to the operation of transportation networks are isolated from

each other.

The three key layers in the function interface are System Layer (Operation), Controller Layer and Function Layer. In the

User Layer, processed information and decision making processes are further divided into three categories

Government/Transport Authorities, Transport Operators and Passengers.

I. System Layer (Operation)

Using the generated models and processed information from the information interface, system-wide decision making can

be implemented at this stage to determine the overall condition of the public transportation system. Filtered information

and general instructions on the current state of the public transportation conditions can then be distributed to the User

Layer. For instance, the need for diversion of vehicles from a region can be set up as a general flag in this layer.

SYSTEM-WIDE INTEGRATIONOF INFORMATION

Government /Transport Authorities

Land-use and Transport Planning

Traffic Signal Control

Policy and Regulation Planning

Benchmarking / PerformanceIndicator

Transport Operators

Vehicle Management

Personnel Management

Seamless Transits

Flexible Fares

Dynamic Routing

Passenger

Information-Assisted JourneyPlanning

Route Condition Awareness

Function LayerUser LayerSystem Layer (Operation)

Figure 3 : System Schematic Showing Operation Interface of the Proposed Intelligent Public Transport System


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II. Controller Layer

In the User Layer, the three users Government/Transport Authorities, Transport

Operators and Passengers can act on the information and instruction sent by the system

layer. They will generate specific instructions and conduct decision-making processes to

control the various subsystems that they are tasked with. For instance, after the system flags

the need to divert vehicles from a region, the Transport Authorities controllers might set up

automated road warning signboards to divert vehicles away from a region using electronic

signboard systems.

III. Function Layer

The function layer consists of all of the ITS division and subsystems that are incorporated into the transportation network

to optimise the transport network. Subsystems are divided according to their controller under the User Layer. In this

section, the report will discuss briefly on the role of each subsystem.

1. Government/Transport Authorities

Land-use and Transport Modelling Division Information report generated by the information interface can be

used by land and transport planners to create urban models that capture transport trends and predict future

population behaviour. This allows planners to make optimal decisions in urban planning.

Dynamic Traffic Signal Control Subsystem This subsystem controls the period and phase relations between

different traffic junctions depending on the traffic controls. This is to optimise the use of green signal time for

public vehicles and to create a smoother driving experience for private car drivers.

Policy and Regulation Planning Division Information reports generated by the system interface can be used to

gauge if the current traffic regulations or policy for the traffic load is appropriate. For instance, transport decisions

such as traffic calming8

for a roadway can be taken after reviewing results from the system.

Benchmarking / Key Performance IndicatorSubsystem Information collected from the system can also be used as

a benchmark to determine the efficiency of the transport network as a whole and the effectiveness of different

improvements and implemented policies

2. Transport Operators

Fleet and Personnel ManagementSubsystem This subsystem uses the assessment of ad-hoc, short-term and

long-term demands provided by the information interface to advise and help transport operators plan vehicle and

personnel resources.

Seamless TransitSubsystem Using the information provided by the system-wide entity, this subsystem will beable to minimise variation in transit journey times based on expected demand levels and current traffic

conditions. As a result, it is possible to plan for seamless transit between different public transportation modes

(inter-transportation and intra-transportation) and between different transport operators. Passengers requiring

transits can be shifted from one destination to another with minimal disruption and crowd levels at transit points

can be reduced.

Flexible Fares With sufficient information, transport operators will be able to implement flexible fares for

commuters who take public transport during peak hours or non-peak hours. In addition, passengers who transit

will not have to pay the full fare of another trip. This has shown to encourage public transportation ridership

levels in many cities.

8Traffic calming refers to the process for regulating vehicles such that traffic flow is slower

Figure 4 : Electronic Signboard

Systems


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Dynamic Routing Subsystem With a robust real-time information interface, buses will be able to take up semi-

dynamic bus routes where certain bus stop points and congested road networks could be avoided entirely. This

information will also be made available to commuters at the various bus stops.

3. Commuters

Information-Assisted Journey Planner Such systems are very much like the applications already provided in

platforms such as the Android and IPhone. However, with the information interface processing information in

real-time, commuters will be able to get more reliable journey predictions and estimations.

User Service Request Using booking systems, commuters will be able to pre-book public transport journeys prior

to the trip. This can be forecasted weeks or months before the actual journey and allow transport operators to

better plan their resources.

C. System-wide Entity

Through merging the information and operation interface, information flow to decision-making process is streamlined into

a system of systems. Information gathered from distributed sensors will be processed into instructions and the interplay

of reactions between the public transportation network and the instructions will provide more information into the

system. This closed-loop system allows transport authorities and transport operators to continuously upgrade and update

their system. An implementation framework is available in the appendix.

Operating

Environment

Physical Road/ TrackNetwork and Conditions

Scheduled ExternalEvents

Real-Time Traffic StatusTravel Profile of Other

Vehicles

DemandAssessment

Passenger Travel HistoryCrowd Density Levels

Event MonitoringPrimitive Location of

User

ResourceMonitoring

Vehicle Location andStatus

On-board PassengerCount

Staff DeploymentResource Planning

SYSTEM-WIDEINTEGRATION OF

INFORMATION

Government /Transport

Authorities

Land-use andTransport PlanningTraffic Signal ControlPolicy and Regulation

PlanningBenchmarking /

Performance Indicator

Transport

Operators

Vehicle ManagementPersonnel

ManagementSeamless Transits

Flexible Fares

Dynamic Routing

PassengerInformation-Assisted

Journey PlanningRoute Condition

Awareness

Raw Information Layer

Domain LayerUser Layer

Function Layer

Figure 5: Schematic Showing Entire Layout of Intelligent Public Transportation System


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5. A VISION OF FUTURE PUBLIC TRANSPORTATION

Autonomous vehicles, green transport and smart cities will shape the future of urban transport. However, given the

current technology infrastructure and levels, there is a significant gap between the envisioned future technologies and

what we have now. This gap can be bridged by the proposed integrated information system and in particular, through two

revolutionary technologies in mass transport Dynamic Bus Routing Subsystem and User Service Request.

Through the simultaneous implementation of these two subsystem, transport operators will be able to gauge transport

demand ahead of schedule and plan buses with dynamic routing to fulfil the areas with increased demand. Theses buses

could operate with no bus numbers and offer ad-hoc routes to fulfil sudden variationsin travel demands.

These buses can operate parallel to the current fixed-route bus systems and commuters could hop onto such buses

through directions given by the User Service Request subsystem. As a result, transport operators will be able to fulfil more

service requests while maintaining a smaller pool of resources, increasing efficiency and reducing wastage.

This vision will require a rethink of how public transport works and to subsequently y change the publics general mind-

sets. However, lessons from other cities that have implement ITS solutions have shown that providing the public with

adequate information will result in people begin more acceptable to such changes.9

6. CONCLUSION

In conclusion, improvements in the area of public transportation are pertinent to a citys future developments. However,

given that it is resource-challenging for many cities who have land constrains and high private vehicle ridership to utilise

current improvements, it is necessary for city and transport planners to look at the possibility of capitalising on

informational resources to improve public transportation.

Currently, many of the subsystems have already been implemented in the various transportation systems worldwide. Such

subsystems have provided a significant boost to the profitability of the transport industries and commuters travel

experience.

Therefore, the time is now ripe to harvest the vast informational resources that ITS systems are generating through using

a system of systems. Indeed, cities that are looking into such system of systems will be likely to be leading figures in the

world.9Refer to reference item 7

No User No User

Conventional

Routing

No User No UserDynamic Routing

Bypass

Bypass


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APPENDIX A CONGESTION FACTS AND FIGURES:

Publics Opinion to the Extent of Congestion in the United Kingdom

A majority of UK residents feel that congestion is a problem in the United Kingdom. Opinion poll shows that over 70% of

the population feel that congestion is a serious or very serious problem. In addition, many of them do not expect

congestion to improve over the next 2 years.10

IBMs Review of Congestion in Different Countries Worldwide

IBM conducted an international survey called the Commuter Pain Survey to find out more on commuters general

transportation satisfaction levels.11

It can be observed in this diagram that developing countries have a greater

transportation challenge to overcome. Already 70% of the populace in UK find that congestion is a problem, hence, one

can only imagine the severity of congestion in the other cities.



Figure 6: Table showing the Percentage of UK Residents on their

Opinion of the Severity of CongestionFigure 7: Opinion Poll about the Predict Extent of Congestion Over the

Next 2 Years

Figure 8: IBM's Consumer Poll of Congestion in Different Cities


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APPENDIX B INTELLIGENT TRANSPORTATION SYSTEMS:

Automatic Vehicle Location Subsystem

In an automatic vehicle location system, transit vehicles are equipped with GPS systems that relay the information

through a receiver station that passes the information back to a dispatch centre.

12Traffic Signal Priority Subsystem

This diagram shows a similar kind of traffic signal priority subsystem that is being used in Zurich. As the transit vehicle

draw nears to a junction, the emitter notifies a traffic signal controller. The controller then determines the most

appropriate traffic light status for the transit vehicle to pass with the least time spent.


Figure 9: Diagram on Operation of Automatic Vehicle Location

Figure 10: Diagram Showing an Implementation of Transit Priority


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APPENDIX C INTERVIEW WITH MR. DANIEL QUEK, TRANSPORT PLANNER, LAND TRANSPORT AUTHORITY, SINGAPORE

Our Ref: LTA/P&P/TPL/SPL/F20.000.000/799

Date : 14-Dec-2011

Tel : 63961843

Fax : 63961754

Dear Mr LeowEnquiries on Traffic and Public Transport Modeling

FEEDBACK NUMBER: 20111205-0100

We refer to your email of 05 December 2011.

We studied your questions and provided our responses in blue below.

General Traffic Modeling:

1. How is LTA currently modeling general transport network demands and usage?

For middle to long term planning, LTA has developed a multi-modal transport model forecasting the future travel demand

based on planning parameters, population and employment distribution provided by other Landuse agencies. The model

forecasted both private transport and public transport demand. The model is calibrated using various data sources

including travel surveys, traffic counts and ticketing system (EIFS - Electronic Integrated Fare System). The EIFS system is a

database containing journey information of public transport users through the use of their contactless cards for publictransport fare payment. The information is used to understand the travelling patterns and behaviour of Singaporeans.

2. Is the model updated with real-time information?

The model is not updated in real time. However the model is updated every year.

3. Is the system integrated with other systems that optimise public transport resources such as MRTs and Taxis?

LTA has a regulatory arm that monitors the operating and service quality standards. LTA monitors the public transport

operators performances with respect to these standards to ensure compliance. These standards may be monitored daily

or monthly and reported monthly or quarterly.

Public Transport Modeling:

1. How does LTA predict public transport usages?

Please refer to response to question 1 above.

2. When did Singapore first start modeling public transportation networks and how successful was the system?

Singapore started public transport modelling in the early 90s and has sought for continual improvement in this field. We

are able to use the model to plan for infrastructural improvements required to meet the growing travel demand over the

years. The model is also used to derive information for economic and financial evaluation of new infrastructure to

facilitate decisions making.

3. What sort of information is used in the previous model and the new models?New sources of information are included in the calibration process to enhance the accuracy of the transport model

whenever it is relevant and available. Examples of this information include the EIFS data and ERP data.

4. How accurate have the current models been?

We have target to have an accuracy of about 90% for various stages of the transport modelling process.

We hope the above information is useful for you and we thank you for writing in.

Yours sincerely

DANIEL QUEK GIM SAN

TRANSPORT PLANNER


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APPENDIX D 2019 INTELLIGENT PUBLIC TRANSPORTATION SYSTEM IMPLEMENTATION FRAMEWORK

DYNAMIC PUBLIC TRANSPORTATION SYSTEM PROJECT DEVELOPMENT AND IMPLEMENTATION 2019 FRAMEW

DEVELOPMENT

STAGE

SUB-PHASE PHASE 1 PHASE II

SYSTEMS FOCUSJan

2013

May

2013

Sep

2013

Feb

2014

Jun

2014

Oct

2014

Mar

2015

Jul

2015

Nov

2015

Mar

2016

Aug

2016 2

PLANNING

AND

EVALUATION

OF OVERALL

SYSTEM

Evaluation

Trams / Train

Buses

Cabs / Taxi

Mobility-on-demand

Integration

Framework

Planning

Existing Subsystems

Proposed

Subsystems

Integratability

Policy

Planning and

Revising

Educating Public

Policy Planning

Contract Tendering

INITIAL

INTEGRATION

STAGE

Raw Information

Layer

Prototyping /

Trial

Area-wide

Implementation

Function Layer

Prototyping /

Trial

Area-wide

Implementation

Domain LayerInitial InformationIntegration with

Raw Information

Layer Subsystem

User Layer

Initial Information

Integration with

Function Layer

Subsystems


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SYSTEM-WIDE

INTEGRATION

Overall System-wide Entity Integration

Evaluation of System-wide Entity

SCALING OF

INFORMATION

SYSTEMS

Upgrading Exisiting Infrastructure

Integration with other Transport

Frameworks


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C P D i P bli T i S

REFERENCES

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Group Proposal on Dynamic Public Transit System

Documents

Transcript of Group Proposal on Dynamic Public Transit System