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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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Concept Paper on Dynamic Public Transportation System
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 Interview with Land Transport Authority, Singapore
IV. Appendix D 2019 Implementation Framework
V. References
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1. INTRODUCTION
Many countries now face the challenge of mass transportation while having to deal with severe congestion within their
cities. Increasing affluence has resulted in the extensive usage of private vehicles and this growth 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 infrastructures were
often done however 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 authorities, transport
operators and commuters.
2. CURRENT INTELLIGENT TRANSPORTATION SYSTEM (ITS)
There are many governments around the world that utilise information and communication technologies to increase the
efficiency of road networks. Examples of technologies that have been implemented include dynamic traffic lights, road
monitoring, demand modelling and passenger journey planners that have 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 operates 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 from as early as in the 1990s! Even though the information is only updated once a year, it has an accuracy of up
to 90%3
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. This is then used 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 second4.
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.
1In 2008, there were 218,000 vehicles per km of road in Singapore. Refer to reference item 1
2 Refer to reference item 113Refer to Appendix C
4Refer to reference item 3
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C. United Kingdom, London Transport for London (TFL)
In London, TFL has implemented an ITS, Automatic Vehicle Location, into an overall bus traffic priority system known as
iBus. This system uses GPS detectors to determine the position of the bus and processes 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 the TFLs website5.
TFL also monitors the usage of different public transport modes through the conduction of the London Travel Demand
Survey6. Here 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 (CTS)
On the 9th
January 2012, our project group was given a rare opportunity to interview CTS at their European Headquarters,
where the London Oyster Card Payment System was developed. CTS handles more than 1 billion passengers every year
and manages up to 50 million pounds of public transport revenue daily. The interviewees were Mr. Matthew J. Cole, Sr.
Vice President of Strategy and Business Development and Mr. Martin Howell, Director of Worldwide Marketing and
Communications.
CTS agree 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 a centralised payment system could
extend to cover various modes of transportation payments, such as payment methods using mobile devices and on
account-based payment systems which provide travelling information to passengers.
Currently, Oyster card readers on buses store transaction data during the day and upload the transaction information to
the back office after the journey, which readers at Tube stations update to the database on 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 due to not
being part of 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, thats 1021
bytes!7
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.
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.
5This is available through Transport for Londons Journey Planner6Refer to reference item 16
7Refer to reference item 14
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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 to allow transport system administrators to get an
understanding of a system-wide health status of the transport infrastructure. Moreover, collected data must be
interpreted level-wise, to reduce computational demands at data processing layers which are higher up in the
informational hierarchy.
The three key layers in the proposed informational interface are the Raw Information Layer, Domain Layerand 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. This information is then
analysed and integrated 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 is 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
while also providing 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 the various sensors that can be/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, while allowing 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
operators8. 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 mean that road availability are affected at times, for instance
road closures during the New Year Countdown. 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 speeds and traffic incidents is
collected in real-time by transport authorities or companies. Current sensing techniques include speed monitoring
8Refer to reference item 17
Figure 2: Operations Office of the Intelligent
Transport System Centre in Singapore
Figure 3: Automatic Road Signs
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cameras, induction loops between traffic junctions measuring the average vehicle travel time and GPS-equipped
vehicles9.
Under DevelopmentTravel Behaviour of All Other Vehicles on Road/Track Vehicle tracking technologies using
image recognition, built-in GPS systems and RFID tags provide the possibility of observing the behaviours of all
vehicles on the road network.
An on-going project is the eCall10. 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 so
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 using incentives such as
subsidised transport costs to attract commuters to record their journeys in a travel record card. However, this
method does not sample the entire commuter population, nor does it 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. This can be used in demand monitoring for
statistical models.
By having a record of travel history, transport operators will be able to charge a 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 to and from school/work are captured in the system. However, unexpected changes indemands 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. Automatic crowd-sensing devices have been built and are now pending patent application.11
9Refer to reference item 210
Refer to reference item 1211
Refer to reference item 18
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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 topredict short-term changes in passenger commuting demand within 15
to 120 minutes.
Event Monitoring Information about events happening across the city should be taken into account when
predicting the 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 of transport demands have been recorded and investigated. This information can be used topredict
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 provide a
rough estimate on the level of crowd in an area. If the travel history of such commuters is available, this
information can be used topredict levels of passenger flow for return journeys. For instance, we can predict thedeparture time of any individual alighting at South Kensington by the general trend of their travel history.
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, such
as the NextVision system that is being planned by Cubic Corporation. Instead of waiting at the bus stop for their
buses, commuters could instead pay for 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. 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 train journeys and flights due to high resource costs
and low passenger counts per route. This technology could be implemented for public transport, should it bedeemed convenient enough for the general user and mutually beneficial to both commuters and transport
operators.
In DevelopmentLocation of Users In the near future, location-based information of users could be accessed
via 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, as well as to plan future improvements in transportation.
Vehicle Location and Status Transport operators have implemented fleet management systems to monitor their
capability, in order 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.
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On-board Passenger Count Information in the on-board passenger count provides transport operators an idea of
the load on their fleets, allowing them to adjust supply.
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 a certain
degree of redundancy to the system as subsystems which 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 Layerand 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.
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 4 : System Schematic Showing Operation Interface of the Proposed Intelligent Public Transport System
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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.
II. Controller Layer
In the User Layer, the three users Government/Transport Authorities, Transport Operators and
Passengerscan 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 can decide to 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 the ITS divisions 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 briefly discuss 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 ControlSubsystem 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 calming12
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, 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 be
able 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
12Traffic calming refers to the process for regulating vehicles such that traffic flow is slower
Figure 5 : Electronic
Signboard Systems
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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 off-peak hours. In addition, passengers with indirect
journeys will not have to pay the full fare of another trip. This has shown to encourage public transportation
ridership levels in many cities.
Dynamic Routing Subsystem With a robust real-time information interface, buses will be able to use 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 collection of subsystems. Information gathered from distributed sensors will be processed into instructions. 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.
Figure 6: 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 subsystems, transport operators will be able to gauge transport
demand ahead of schedule and plan buses with dynamic routing to meet the increased demand in certain areas. These
buses could operate with no bus numbers and offer ad-hoc routes to fulfil sudden variationsin travel demands.
These buses can operate in parallel with the current fixed-route bus systems so commuters are able to 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, increase efficiency and reduce wastage while still maintaining a smaller pool of resources.
This vision will require a rethink of how public transport works and to subsequently change the publics general mind-sets.
However, we have learnt from other cities which have implemented ITS solutions that providing the public with adequate
information will result in people growing more agreeable to such changes.13
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 under land constrains and high private vehicle ridership to utilise
current improvements, it is necessary for transport authorities to consider of capitalising on informational resources in
order to improve public transportation.
Currently, many of the subsystems have already been implemented in various different transportation systems
worldwide. Such subsystems have provided a significant boost to the profitability of both the transport industries and the
commuters travel experience.
We believe that the time is now ripe to harvest the vast informational resources that ITS systems are generating. The
efficient processing of such information will facilitate fast transport networks. It is therefore essential that world leading
cities incorporate such a system.
13Refer 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.14
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.15
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.
14Refer to reference item 5
15Refer to reference item 10
Figure 7: Table showing the Percentage of UK Residents on their
Opinion of the Severity of CongestionFigure 8: Opinion Poll about the Predict Extent of Congestion Over the
Next 2 Years
Figure 9: 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.
16Traffic 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.
16Refer to reference item 6
Figure 10: Diagram on Operation of Automatic Vehicle Location
Figure 11: 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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Concept Paper on Dynamic Public Transportation System
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
8/3/2019 Dynamic Public Transportation System_Group Proposal
17/18
Imperial College London Electrical and Electronic Engineering
Concept Paper on Dynamic Public Transportation System
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
8/3/2019 Dynamic Public Transportation System_Group Proposal
18/18
Imperial College London Electrical and Electronic Engineering
C P D i P bli T i S
REFERENCES
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(13) Ryan C.C Chin. Reinventing the Automobile: Personal Urban Mobility for the 21st Century. MassachusettsInstitute of Technology - Media Lab; 2011.
(14) Samuel J. Palmisano. A Smart Transportation System: Improving Mobility for the 21st Century. IntelligentTransportation Society of America, 2010 Annual Meeting & Conference, Houston, Texas, May 5, 2010.
(15) Silvester Prakasam. The Evolution of e-payments in Public TransportSingapores Experience.Japan Railway& Transport Review2008(50).
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(17) Transport for London. London's Register of Roadworks. [Online] Available from:http://public.londonworks.gov.uk[Accessed 12 Jan 2012].
(18) Li-Qun Xu and Arasanathan Anjulan. Crowd Congestion Analysis Patent Application. [Online] Available from:http://www.faqs.org/patents/app/20100322516 [Accessed 20 Feb 2012].
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