Conference PaperPDF Available

Intelligent Public Transportation Systems: A Review of Architectures and Enabling Technologies

Authors:

Abstract and Figures

Intelligent Public Transportation Systems (IPTS) are a subsystem of Intelligent Transportation Systems (ITS), which aim to control public transportation networks, to maintain their performance, and to provide users (passengers and decision makers) with up-to-date information about trips and network operating conditions. To reach these aims, IPTS rely on several technologies that can be embedded within different control architectures. This paper introduces IPTS components and technologies, identifies the different types of data captured from the transportation network and exchanged between IPTS components, and shows ways to integrate technological components and data within IPTS architectures. A section is dedicated to review architectural design of some developed IPTS to control public transportation networks. Finally, some challenges are discussed and further research directions are highlighted.
Content may be subject to copyright.
1
Intelligent Public Transportation Systems: A Review
of Architectures and Enabling Technologies
Sabeur Elkosantini
LOGIQ Research Unit, University of Sfax
Higher Institute of Computer Science of Mahdia
Tunisia
Sabeur.Elkosantini@isima.rnu.tn
Saber Darmoul
Industrial Engineering Department
College of Engineering, King SAUD University
Kingdom of Saudi Arabia
sdarmoul@ksu.edu.sa
Abstract Intelligent Public Transportation Systems (IPTS) are
a subsystem of Intelligent Transportation Systems (ITS), which
aim to control public transportation networks, to maintain their
performance, and to provide users (passengers and decision
makers) with up-to-date information about trips and network
operating conditions. To reach these aims, IPTS rely on several
technologies that can be embedded within different control
architectures. This paper introduces IPTS components and
technologies, identifies the different types of data captured from
the transportation network and exchanged between IPTS
components, and shows ways to integrate technological
components and data within IPTS architectures. A section is
dedicated to review architectural design of some developed IPTS
to control public transportation networks. Finally, some
challenges are discussed and further research directions are
highlighted.
Keywords Intelligent Transportation Systems; public
transportation; technologies; architectures;
I. I
NTRODUCTION
City life and urbanization have introduced mobility
problems and raised issues concerning transportation of both
people and goods. Public transport (also known as public
transportation or public transit) refers to shared passenger
transport service, which is available for use by the general
public. Public transport modes include buses, trolleybuses,
trams and trains, rapid transit (metro/subways/undergrounds
etc.) and ferries. Public transport between cities is dominated
by airlines, coaches, and intercity rail. High-speed rail
networks are being developed in many parts of the world.
Despite the spread and success of implementing public
transport networks, it is becoming more and more difficult to
guarantee high levels of quality of service for users, for
example in terms of punctuality and frequency of shuttles. This
difficulty is due, on the one hand to the continuous
urbanization, which makes transportation networks grow in
size and, on the other hand, to the ever increasing complexity
of managing transportation networks. Implementing new
intelligent decision support and control systems is becoming
necessary both to manage public transportation networks, and
to assist authorities, who are investing in new means,
infrastructures, information and control systems to improve
mobility in cities.
Intelligent Transportation Systems (ITS) have been
introduced to take full advantage of the existing public
transportation infrastructure, and to enhance its efficiency,
effectiveness and attractiveness. ITS are defined as the
application of advanced communication systems, information
processing, control and electronics technologies to improve the
transportation system and to save lives, time and money [1].
ITS for public transportation, also called Intelligent Public
Transportation Systems (IPTS)[2], rely on many innovative
technologies, which fostered their development and
implementation. For example, Geographical Information
Systems (GIS) help in designing new routes and shuttles.
Automatic Vehicle Location Systems (AVLS) rely on Global
Positioning Systems (GPS) to localize transportation means
and vehicles. Traveler Information Systems (TIS) provide users
with real-time information about the state of the network. IPTS
also allow for the integration of Decision Support Systems
(DSS), which suggest regulation strategies and control
decisions to maintain the performance of the network. All of
these information systems and technologies share, process and
exchange several types of data provided by dierent detection
devices, and advanced technologies, such as global positioning
satellites (GPS) and sensor networks.
Although a few works, such as [3] and [2], discussed
technologies used in the field of public transportation network
management, these works are limited to the presentation of
technical aspects and only present technologies to capture data
from the network. These works do not discuss how
technologies and information systems can be integrated within
IPTS architectures, what kind of data and how this data can be
exchanged through the different IPTS subsystems to achieve
the common and ultimate goal of controlling the transportation
network and optimizing its performance.
Therefore, the aim of our paper is to fill this gap by
presenting an updated review of innovative technologies used
in IPTS. We particularly highlight how required data is
captured. We describe how integration between IPTS data and
components is achieved within architectures that are able to
suggest suitable regulation strategies and control decisions to
reach the ultimate goal of controlling the transportation
network and optimizing its performance. Therefore, the
purpose of this paper is not to analyze existing IPTS but rather
to present its different components, architectural layers, and
2
enabling information and communication technologies. The
paper is organized as follows: Section 2 introduces the
intelligent public transportation systems (IPTS). Section 3
presents different communication and information technologies
integrated in IPTS. Section 4 reviews works suggesting
architectures to integrate these technologies within Intelligent
Public Transportation Systems (IPTS). We conclude by
discussing some challenges and highlighting further research
directions related to IPTS design and implementation issues.
II. ITS
FOR THE MANAGEMENT OF PUBLIC TRANSPORTATION
NETWORKS
The European Union (EU) Directive 2010/40/EU defines
ITS as systems in which information and communication
technologies are applied in the field of road transport to
manage infrastructure, vehicles, users (passengers and decision
makers), traffic flows and mobility within cities, and to
interface with other modes of transport. According to this
definition, ITS are made of several subsystems including:
Advanced Traffic Management Systems: ATMS act on
traffic signals to control traffic flows and to enhance
mobility in cities.
Advanced Rural Transportation Systems: ARTS focus
on traffic management in rural environments.
Commercial Vehicle Operation Systems: CVOS monitor
commercial vehicles, trucks and vans using satellite
based navigation systems.
Advanced Public Transportation Management Systems
(APTMS), also called Intelligent Public Transportation
Systems (IPTS) [2] or Advanced Pubic Transportation
Systems (APTS) [4]: these systems rely on a set of
advanced communication, information processing,
control and electronics technologies to control public
transportation networks.
In this paper, we only focus on Intelligent Public
Transportation Systems, which we refer to as IPTS. Our
purpose is to present technologies used in IPTS and to discuss
architectural aspects to show how these technologies can be
integrated within a control system.
IPTS are intended to analyze and evaluate the status of
transportation networks, to detect disturbances (such as
accidents, technical problems, traffic congestion, etc.) that can
affect prescheduled timetables and make them deviate from
their expected performance and/or behavior, and to suggest
efficient regulation strategies and control decisions to maintain
the performance of the network. IPTS provide users with up-to-
date information about the status of transportation networks.
The development and implementation of IPTS improves the
quality of service of public transportation networks and
promotes the use of public transportation means in cities, thus
contributing to reduce congestion and pollution and to improve
mobility. Several measures of effectiveness of IPTS were
proposed and detailed in [5]. These measures include indicators
about safety, mobility, efficiency, productivity, energy,
environment, and customer satisfaction. To reach high levels of
Quality of Service, IPTS rely on a wide variety of technologies
and applications that can be grouped in five main categories:
1. Automatic Vehicle Location Systems: AVLS provide
decision makers with real-time information about
vehicles, such as location, speed and direction of
vehicles, and information about delays due to
disturbances, such as traffic congestion, accidents, bad
weather conditions, or road repair work.
2. Traveler Information Systems: TIS provide passengers
with real-time information about the operating
conditions of the network, such as scheduled shuttles and
arrival and departure times of vehicles.
3. Automatic Passenger Counters: APC count on-board
passengers and those waiting for vehicles at stop
stations.
4. Geographic Information Systems: GIS allow an instant
mapping and follow up of the progress of vehicles on
their routes. GIS also allow for the design and
implementation of new routes and shuttles.
5. Decision Support Systems: DSS assist decision makers
in controlling the transportation network by suggesting
control decisions and regulation strategies when
unexpected events or deviations from expected
performance and/or behavior occur.
These technologies will be presented in more detail in the
following section.
III. T
ECHNOLOGIES FOR
IPTS
Several technologies allow IPTS to retrieve data from
multiple sensor systems, to supervise and control the
transportation network. Information and communication
technologies are widely used in IPTS. Vehicles, stations,
operation centers and other transportation infrastructure are
equipped with recent technologies including tracking systems
(such as GPS or radio navigation), infrared beams, wireless
equipment and communication infrastructure (GSM and GPRS
networks). These communication systems enable vehicles,
passengers and decision makers to interact with the different
components of the IPTS as illustrated in figure 1.
These technological devices enable decision makers to
monitor the network performance and to make suitable control
decisions to insure good operating conditions. This section
identifies and presents information and communication
technologies integrated within an IPTS.
3
GPS
antenna
GPS satellite
Cellular
TIS
AVL
VMS
CCTV
DSS
Communication
network
GIS
Modem
APC
Operation Center
DSS :decision Support System, AVL : Automatic Vehicle Location,
TIS : Traveler Information System, VMS : Variable Message Sign,
APC: Automatic Passenger Counter, GIS : Geographic Information
System, CCTV: Closed Circuit TeleVision
Figure 1. Intelligent Transportation system
A. Automatic Vehicle Location Systems
Automatic Vehicle Location Systems (AVLS) provide
information regarding the exploitation of a transportation
network. In the scientific literature, such systems are often
referred to under several names, such as Automatic Vehicle
Monitoring Systems AVMS [6], Automatic Vehicle Location
AVL [7], Exploitation Aid System EAS [8-9] or Exploitation
Support System ESS [10]. In this paper, we refer to these
systems as AVLS.
Such systems give a global overview of a transportation
network and provide real time information through the use of
several vehicle location and tracking technologies. Data
provided by AVLS includes updated states of timetable
execution, delays, and vehicles in advance. The
implementation of AVLS has greatly facilitated the task of
decision makers because AVLS can monitor real-time
operation of a public transportation network and process a very
large amount of network information.
One of the first forms of vehicle tracking technologies
being used was the ground based radio system (GBR). It
determines location based on the reception of signals and the
associated timings from various transceivers. As reported in
[11], the accuracy of this system is not consistent especially in
urban areas as they are highly susceptible to radio frequency
and electromagnetic interference from power lines and
substations in urban and industrial areas.
Signpost and Odometer were also among the primary
technologies used for vehicle tracking. With this technology,
receivers are placed on vehicles, while transmitters are placed
along vehicle routes. Vehicles transmit a low-powered signal
as they pass by these transmitters, and the mileage is noted.
AVLS based on this technology have some drawbacks. For
example, Signpost transmitters require periodic maintenance
(to replace battery for example). Furthermore, the creation of
new routes requires the placement of new transmitters.
Due to limitations of GBR and Signpost/Odometer
technologies and with the development of applications in
electronics and digital communications, Global Positioning
Systems (GPS) became the most popular systems for vehicle
tracking [12]. GPSs are space-based satellite navigation
systems that provide location and time information in all
weather, anywhere on Earth. This system was developed by US
department of defense to serve the military need to locate
vehicles. The system uses a total of 24 satellites [19]. In
transportation systems, vehicles are equipped with a GPS
antenna, which communicates with four or more satellites to
give the location of the vehicle.
Galileo is another global navigation satellite system
developed by the European Union to provide high-precision
positioning independently from the American GPS. Some
European IPTS use this navigation system such as CIVITAS
[14]. Satellite based navigation systems give a good precision
only with the presence of line of sight between the receiver and
satellites. Otherwise, the signal will be attenuated and, thus,
vehicles cannot be tracked. Due to this limitation, RFID
technology is also used as vehicle tracking systems [15].
Other technologies are integrated in IPTS to locate vehicles
in the network. For example, Closed Circuit TeleVision
(CCTV) are coupled with image processing techniques to
monitor vehicles in the public transportation network of
London by the use of 2000 cameras installed in the routes and
stations [16].
B. Traveler Information Systems
ITS for public transport integrate technologies to provide
information to travelers or to operation centers. A “Traveler” is
defined as a person who changes location by any transportation
mode. Some authors also consider vehicle drivers as travelers
[17], while others only consider passengers as travelers [2].
The main goal of Traveler Information Systems (TIS), also
referred to as Real-time Passenger Information System RTPIS
[18], is to provide real time information to travelers about the
state and operating conditions of the network, such as vehicles
arrival time, and assist them to allow informed pre-trip and en
route decision making.
According to Adler and Blue [19], two generations of TIS
exist. The first one is the Variable Message Signs (VMS) that
provides information about vehicles. VMS are used in stations
to provide travelers with important information about the
network, such as vehicle waiting time or presence of incidents.
Data are sent to VMS via communication infrastructure, such
as GSM [53] or wireless network [20].
The second generation is the Advanced Traveler
Information System (ATIS), which uses recent technologies,
such as internet or mobile phones, to provide information about
traffic conditions, route guidance and en route traveler
information in a more real time manner.
Several TIS were developed to assist travelers in making
pre-trip and en route travel decisions, such as Intelligent
4
Traveler Information Systems ITIS by Adler and Blue [19],
Path2Go by Zhang et al. [21], and work by Praveen et al. [22].
RAPID is another commercial TIS developed by Sigtec
1
company using SMS, web and street displays. RAPID solution
incorporates an AVLS system to send up-to-date information
about vehicle arrival times to passengers.
To provide more efficient data on vehicle travel or arrival
time estimates to passengers, TIS are based on AVLS [23. For
example, SITREPA [24] is an IPTS which integrates an AVLS
and TIS and tested in the city of Leiria (Portugal). As other
IPTS, SITREPA acquires data from AVLS and provides
information to satisfy the needs of different actors in the
public-transportation system as passengers or decision makers.
C. Automatic Passenger Counters
Automatic Passenger Counters (APC) are systems that
count on-board passengers and those waiting for vehicles at
stop stations. Such information can be used to analyze the
global performance of the transportation system [25]. It can be
used to calculate average vehicle travel speeds and dwell times
[26]. APC can interface with AVLS to provide transit agencies
with transit origin-destination data [26].
The first generation of APC was based on manual ride
checks to collect the necessary data on boarding and alighting
activities. Recently, communication technologies are widely
used to develop more efficient APC. Such technologies include
treadle mats and infrared beams, which recognize passengers
when the beam is broken. Computer imaging is also used,
which is based on intelligent image detection systems to
recognize and count on board passengers [16]. [27] evaluated
the performance, in terms of accuracy and precision, of on-
board camera and other APC systems. They reported that
camera systems are more precise than on-board ride checkers.
D. Geographic Information Systems
Geographic Information Systems (GISs) capture, store,
manipulate, analyze, manage, and present all types of
geographical data related for example to vehicle route design
[27]. The first task of a GIS is to code data collected by
tracking systems, such as GPS or Galileo systems (see
subsection A). Thus, the connection of GIS to GPS allows
instant mapping and follow up of the progress of vehicles on
their routes, and localization of disturbances on the
transportation network [29]. In many ITS, GIS is also used for
analyzing the traffic flow [30], for evaluating and ranking
vehicle service [31] and for transportation network design [32].
In the last few decades, researches focused on automating the
route-planning process using GIS technology [33]. Some
commercial GIS software, such as ArcGIS [34] or MapInfo
[35], exist and can be used to develop digital maps and to
realize basic GIS functions.
E. Decision Support Systems
Decision support systems (DSS), also called Scheduling
and Dispatching Software [36], have two main objectives:
timetable establishment and control strategies building.
1
http://www.sigtec.com
The first common objective of DSS is the establishment of
efficient transportation timetables that satisfy passengers, who
expect high levels of quality of service, in terms of timely and
regular shuttles. Transportation timetables are initially
established taking into account information about forecasts of
traffic conditions, rush hours, demand for transportation, etc.
[6]. Several works use either exact [37] or heuristic [38]
methods to determine timetables that optimize one or several
objectives, such as minimizing total trip time or cost [39],
minimizing passenger waiting time, or minimizing passenger
in-vehicle time [38].
However, during the execution of pre-established
timetables, disturbances may appear that can make these
timetables deviate from their expected course, causing them
either to be delayed or to become obsolete [40]. When they
occur, such disturbances like accidents, traffic congestion,
absence of personnel, bad weather conditions, etc., degrade the
expected performance of the transportation network, decrease
its expected quality of service, yield to passenger
dissatisfaction, and may cause the appearance of congestion at
stations or on transportation pathways. Consequently, decision
makers have to monitor the execution of pre-established
timetables, and to make reaction decisions in order to bridge
the gap between pre-established timetables and really executed
ones.
The second objective of DSS is to maintain the
performance of pre-established timetables at acceptable levels.
DSS have to analyze incoming data from Automatic Vehicle
Location systems (AVLS) and Automatic Passenger Counters
(APC) in order to detect serious delays of vehicles. When such
is the case, DSS have to suggest suitable control decisions and
regulation strategies to eliminate or at least reduce deviation
from predefined timetables. Several works suggested control
decisions; including holding strategies [37-49] and stop
skipping strategies [39]. DSS can receive information from
special equipment, such as panic buttons. In some cities like
Washington DC in USA [41] or La Rochelle in France [42],
stations are equipped with panic buttons that can be activated
by passengers, operators or drivers to alert passengers and
operation centers to take immediate action.
According to [43], DSS must integrate three main phases:
Diagnostics phase: it consists in monitoring and
analyzing the transportation network to detect
disturbances, anomalies and deviations from expected
performance and/or behavior using AVLS.
Decision construction phase: the system suggests
control decisions and regulation strategies for the
detected disturbances, anomalies and deviations.
Decision evaluation phase: control decisions and
regulation strategies are evaluated using simulation
[44] or exact methods [45] to select the best alternative
to be applied.
Several studies were proposed to design DSS to control
public transportation networks, such as TRSS [6], MASDAT
[46], SMAST [47], and systems by Masmoudi et al. [9],
Tlig
and [48], and [49]. Such systems receive information from
5
AVLS, TIS and APC via radio or other communication
technologies, such as cellular phones or modems.
IV. IPTS
A
RCHITECTURES
Several studies were proposed to design IPTS architectures.
Davidsson et al. [50] pointed out that, at least until year 2005,
64% of the existing research focused mainly on design issues
and architectural aspects. These architectures integrate a
variety of information systems that receive data from sensors.
As illustrated in figure 2, these data concern vehicles (vehicle
location, routes, direction, next station, and accidents),
passengers (waiting or on-board passengers) or other incidents
(technical problems). These data are sent to IPTS subsystems
such as DSS or TIS using communication networks as GSM,
Modem or Wireless networks. The DSS analyzes received data
to monitor the execution of pre-established timetables and
make reaction decisions in order to bridge the gap between pre-
established timetables and really executed ones.
Most of developed IPTS implement Interactive Decision
Support systems, considered as the core of an IPTS, integrating
the decision maker in the decision loop (figure 2). With respect
to the integration of decision makers in the loop, [51] identify
two types of cooperation between decision makers and decision
support systems: horizontal and vertical. In the horizontal
cooperation, decision makers and DSS dynamically share the
tasks to be per-formed. In such architecture, traffic data
provided by AVLS and APC are only analyzed by the DSS that
generates the best decision. This kind of cooperation is used in
autonomous systems in which the only task of decision makers
consists in supervising the decision making process. However,
in a vertical cooperation, DSS can be considered as a guide to
the support decisions. In such cooperation, decision makers
assist the system. Decision makers can interact with the system
in each step of the information processing or the decision
making procedure.
Several public transportation agencies over the world have
implemented intelligent transportation systems to insure a high
quality of service to passengers.
For example, [52] reported that more than 122 agencies
have implemented an IPTS in USA. They reported also that
Geographic Information Systems (GIS) and Decision Support
Systems (DSS) were the most widely used technologies.
Another example, CIVITAS [42] is an IPTS funded by the
European Commission and implemented in 60 European cities,
including La Rochelle, London or Frankfort. Within CIVITAS,
different components of Public Transport Information systems
were implemented:
TIS via boards and terminals, on and off the public
transport system, via SMS and/or e-mail
Internet services providing public transport
information
Over ground network map and mini map available
on board public transport vehicles and other
campaigns and information material to promote
public transport
Real-time traffic and parking information for
drivers via Variable Message Signs
Intelligent Public Transportation System
Traveler Information
System
Automatic Vehicle Location
system
Geographic
Information Systems
Decision Support System
On-board
passengers number
Vehicles position, Direction
Routes, Next station
Served station
Distrubances
Timetable updates
Passengers
VMS
Cellular
or
PDA
Decision maker
Suggested
Regulation
strategy Modification
Figure 2. Information systems for IPTS
[6] developed an IPTS, named TRSS, designed according
to a vertical architecture. The system integrates an AVLS
(using GPS system), which provides vehicle tracking
information to a multi agent decision support system.
SITREPA [24] is another IPTS tested in the city of Leiria
(Portugal). The system combines GPS and RFID technologies
to locate vehicles on the network. It integrates a TIS to provide
real time information about the network.
V. C
ONCLUSION
The main objective of this paper is to identify technologies
on which intelligent public transportation systems (IPTS) rely
to control transportation networks. We identified several
technologies, such as Traveler Information Systems,
Geographic Information Systems, Automatic Vehicle Location
Systems and Decision Support Systems, which are all based on
advanced information and communication technologies. These
systems exchange different types of data, such as vehicle
location, messages, alerts and videos. With respect to existing
works, such as [2] and [3], our survey focused on highlighting
architectural integration of data and technologies rather than
presenting technical aspects of information and communication
technologies.
6
Our literature survey shows that several directions must be
explored to improve the integration of all advanced information
systems. Due to the number of subsystems, the diversity and
the quantity of exchanged data, IPTS must insure a high
interoperability level in existing architectures. Therefore, new
subsystems must be developed and integrated in architectures,
which have to automatically analyze all type of data and detect
events that will affect the performance of the network. Thus,
they must be as generic as possible to detect any type of
disturbing event. To the best of our knowledge, this direction is
not well explored and no generic tools were proposed.
Furthermore, such subsystems must be compatible with actual
IPTS and support existing technologies without changing
developed architectures.
R
EFERENCES
[1] C. Strong and S. Albert, California-Oregon Advanced Transportation
Systems ITS Strategic Deployment Plan, Final report, Western
Transportation Institute 2002.
[2] Trans-ITS, Intelligent Public Transport Systems : State-of-the-art in
Europe, International Association of Public Tr ansport, 2002
[3] A. John and S.M. Halkias, “Advanced Transportation Management
Technologies Traffic Control Systems”, Office of Transportation
Management, October, 1997, Chapter 6, pp 6-1 – 6-23
[4] L. Vanajakshi, Intelligent Transportation Systems, Synthesis Report on
ITS Including Issues and Challenges in India, dept. of Civil Engineering,
IIT Madras, December, 2010.
[5] Mitretek Systems, Intelligent Transport System Benefits: 2001 update,
Under Contract to the Federal Highway Administration, US Department
of Transportation, Washington DC. (US), 2001.
[6] F. Balbo, and S. Pinson, “Using intelligent agents for Transportation
Regulation Support System design”. Transportation Research Part C:
Emerging Technologies 18(1), 2010, 140–156.
[7] B. Yu, S. Wu, B. Yao, Z. Yang, and J. Sun, “Dynamic Vehicle
Dispatching at a Transfer Station in Public Transportation System.” J.
Transp. Eng., 138(2), 2012, 191–201.
[8] S. Canela and M. Gil, citymobile, adapted vehicles, Delivrable n 1.4.2
2011.
[9] A. Masmoudi, S. Elkosantini, S. Darmoul and H. Chabchoub. An
artificial immune system for public transport regulation, 9th
International Conference of Modeling and Simulation – MOSIM2012,
2012, Bordeaux, France.
[10] A. Fernández, S. Ossowski and E. Alonso, “Multiagent service
architectures for bus fleet management”, Integrated Computer Aided
Engineering, Vol 11(2), 2004, 101–115.
[11] ICAO, Manual on testing of radio navigation aid, Volume I Testing of
Ground-Based Radio Navigation Systems-4th Edition, 2000.
[12] C. M. Johnson and E. L. Thomas, “Automatic Vehicle Location
Successful Transit Applications: A Cross-Cutting Study: Improving
Service and Safety”, Joint Program Office for Intelligent Transportation
Systems, FTA, 2000.
[13] Z.R. Peng, E.A. Beimborn, S. Octania and R.J. Zygowicz, Evaluation of
the Benefits of Automated Vehicle Location Systems in Small and
Medium Sized Transit Agencies, Tech. Report, Center For Urban
Transportation Studies, 1999.
[14] B. Godefroy, “Demonstration of EGNOS/Galileo services use for the
control and information system of public transport in Toulouse”, Polis
Annual Conference – Toulouse – 15-16 March 2007
[15] H. Ben Ammar and H. Habib, “Bus Management System Using RFID
in WSN”, European and Mediterranean Conference on Information
Systems 2010, UAE, 2010, pp 45-50.
[16] M. Cracknell, J. McCarthy and Derek Renaud, “Image detection in the
real world, Intelligent Transportation”, Systems World Congress ITS
WC 2008, New York, 2008
[17] D. Levinson, “The value of advanced traveler information systems for
route choice”, Transportation Research Part C: Emerging Technologies,
Volume 11, Issue 1, 2003, Pages 75–87
[18] K. Ganesh, M. Thrivikraman, Joy Kuri, Haresh Dagale, G. Sudhakar and
S. Sanyal, “Implementation of a Real Time Passenger Information
System”. CoRR abs/1206.0447, 2012.
[19] J. L Adler and V. J Blue, “Toward the design of intelligent traveler
information systems”, Transportation Research Part C: Emerging
Technologies, Volume 6, Issue 3, 1998, Pages 157–172
[20] L. Peng, F. Xiao and Z. Ni, “Design for Wireless Sensor Network-Based
Intelligent Public Transportation System”, 3rd International Conference
on Anti-counterfeiting, Security, and Identification in Communication,
2009. ASID, 2009, 351- 354
[21] L Zhang, S.D. Gupta, J.Q. Li; K. Zhou and W.-B. Zhang, “Path2go:
Context-Aware Services for Mobile Real-Time Multimodal Traveler
Information”, 14th International IEEE Conference on Intelligent
Transportation Systems (ITSC), 2011, pp. 174- 179
[22] K. Praveen, S. Sanjeev and M. R. Kesh, “Advanced Traveller
Information System for Historical Cities”, Indian Highways, Volume:
40, Issue Number:7, Indian Roads Congress, 2012, pp 17-29.
[23] S.I.J. Chien, Y. Ding and C. Wei, “Dynamic bus arrival time prediction
with artificial neural networks”, Journal of Transportation Engineering,
128 (5), 2002, pp. 429–438
[24] LC Cachulo, CR Rabadão, Fernandes, T.R.; F. Perdigoto and S.M.M.
Faria, “Real-time information system for small and medium bus
operators”, Procedia Technology 5, 2012, pp. 455 – 461.
[25] A. Olafsdottir, “Bus Service Performance Analysis, Case Study: Bus
Line 1 in Stockholm”, Sweden, Msc thesis, School of Architecture and
the Built Environment (ABE), Transport Science, Traffic and Logistics,
2012
[26] K. Passetti, L. Jin and D. DePencier, Deriving Vehicle Travel
Speed/Dwell Time Using Automatic Passenger Counter (APC),
technical report, Florida Department of Transportation Transit Office,
2012
[27] T. J. Kimpel, J. G. Strathan, D. Griffin, S. Callas, and R.L. Gerhart,
“Automatic passenger counter evaluation: Implications for national
transit database reporting”, Transportation research record, no 1835,
2003, pp. 93-100.
[28] R. Thompson, Y.N.Wang and I.D. Bishop, “Integrating GIS with
intelligent transport system and stochastic programming for improved
vehicle scheduling”. Computers in Urban Planning and Urban
Management, Venice, 1999, CD-ROM.
[29] B. Sadoun and O. Al-Bayari, “Location based services using
geographical information systems”, Computer Communications,
Volume 30, Issue 16, 3, 2007, pp. 3154–3160.
[30] W. Shi, Q.J. Kong and Y. Liu,” A GPS/GIS Integrated System for Urban
Traffic Flow Analysis”, Proceedings of the 11th International IEEE
Conference on Intelligent Transportation Systems Beijing, China, 2008.
[31] F. McGinley, “A GIS approach to bus service planning: a methodology
for evaluating bus service proposals”, 24th Australasian Transport
Research Forum (ATRF) , 2001.
[32] C. Feng, H. Wei, and J. Lee, “WWW-GIS strategies for transportation
applications”, Proceedings of the 78th Transportation Research orard,
Washington, DC, 1999.
[33] V. Yildirim, R. Nisanci and S. Reis, “A GIS based route determination
in Linear Engineering structures information management, shaping the
change”, XXIII FIG congress, Munich, Germany October 8-13, 2006
[34] T. Ormsby, E.J. Napoleon, R. Burke, C. Groessl and L. Bowden, Getting
to Know ArcGIS Desktop. 2nd edition. ESRI Press, 2010.
[35] MapInfo Professional v11.5, 2012. User guide. Pitney Bowes
Corporation.
[36] E.T.M. Horn, “Fleet scheduling and dispatching for demand-responsive
passenger services”, Transportation Research Part C: Emerging
Technologies, Volume 10, Issue 1, 2002, Pages 35–63.
[37] X.J. Eberlein N.H.M. Wilson and D. Bernstein. The Holding Problem
with Real-Time Information Available. Transportation Science 35,
2001, pp. 1-18.
7
[38] C. E. Cortés, D. Sáez, F. Milla, A. Núñez and M. Riquelme, “Hybrid
predictive control for real-time optimization of public transport systems’
operations based on evolutionary multi-objective optimization”,
Transportation Research Part C: Emerging Technologies Volume 18,
Issue 5, 2010, pp. 757–769.
[39] LP. Fu, Q. Liu and P. Calamai, “Real-time optimization model for
dynamic scheduling of transit operations”. Transp Res Rec 1857, 2003,
pp. 48, 55.
[40] J. T. Krasemann, Design of an effective algorithm for fast response to
the re-scheduling of railway traffic during disturbances, Transportation
Research Part C: Emerging Technologies, Volume 20, Issue 1, 2012,
Pages 62-78.
[41] FHWA-JPO-98-031, Developing Traveler Information Systems Using
the National ITS Architecture, US departmement of Transportation,
USA, 1998
[42] D. Engels, B. Kontić, M. Matulin, Š. Mrvelj, B. Van Cauwenberge, J.
Valkova, C. Vilarinho and J.P. Tavares, CIVITAS: Final Evaluation
Plan, ELAN Deliverable, 2009, No. 12.1.
[43] I. Boudalia, I. Ben Jaafara and K. Ghedirab, “Distributed decision
evaluation model in public transportation systems”, Engineering
Applications of Artificial Intelligence 21, 2008, pp. 419–429
[44] H. Ezzedine, T. Bonte; C. Kolski and C. Tahon “ Integration of Traffic
Management and Traveller Information Systems: Basic Principles and
Case Study in Intermodal Transport System Management”, International
Journal of Computers, Communications & Control (IJCCC), Vol. 3,
2008, pp. 281-294.
[45] P. Borne, B. Fayech, S. Hammadi and S. Maouche, “Decision support
system for urban transportation networks”, IEEE/SMC Transactions,
Part C, 33 (1), 2003, pp. 67–77.
[46] K. Bouamrane, C. Tahon and B. Beldjilali, “Decision making system for
regulation of a bimodal urban transportation network, associating
classical and multi-agent approaches”. INFORMATICA, Vol 16, N°3,
2005, pp 1-30.
[47] R..D, Rahal and .M.R Chekroun, “Multi-Agent System for Modeling
Transport Systems”, European Journal of Scientific Research, ISSN
1450-216X Vol.46 No.1 2010, pp.080-089.
[48] M. Tlig and N. Bhouri, A Multi-Agent System for Urban Traffic and
Buses Regularity Control, Procedia - Social and Behavioral Sciences,
Volume 20, 2011, pp. 896-905.
[49] C.F. Daganzo, “A headway-based approach to eliminate bus bunching:
systematic analysis and comparisons”, Transportation Research Part
B:Methodological 43 (10), 2009, pp. 913–921
[50] P. Davidsson, L. Henesey, L. Ramstedt, J. Törnquist and F. Wernstedt,
“An analysis of agent-based approaches to transport logistics”,
Transportation Research Part C 13, 2005, pp. 255–271.
[51] J. Rasmussen, B. Brehmer and J. Leplat, Distributed Decision Making:
Cognitive Models for Cooperative Work, John Wiley & Sons Ltd. 1990
[52] J. Hough, C. Bahe, M. Lou Murphy and J. Swenson, “Intelligent
Transportation Systems: Helping Public Transit Support Welfare to
Work Initiatives”, Research Report, North Dakota State University,
2002.
[53] L. Zhian and H. Hana, “Bus Management System Based on ZigBee and
GSM/GPRS”, 201O International Conforence on Computer Application
and System Modeling (ICCASM 2010), 2020, pp.210-213
... Sci. 2024, 14, 11599 4 of 23 user information, geographic information systems and decision-support systems [14]. Two most popular implementations are: ...
... Sci. 2024, 14, 11599 ...
Article
Full-text available
Quality of service in urban surface transit has a great impact on sustainable urban mobility, with travel time being one of the most important indicators of service reliability. But urban surface transit is prone to many disturbance factors causing travel time discrepancy and variability, making transit less reliable for passengers. We conducted research in the City of Zagreb on a single tram line by splitting it into constant-frequency segments. The first phase was modeling minimum segment travel times to base the indicators and predictors upon, and the second phase was establishing correlation matrices between the predictors and travel time using Pearson correlation coefficients and significance. Variance inflation factors were used to check for collinearities. While predictors belonging to the transport supply irregularity group did not have an impact, the ones belonging to the disturbance factors group showed correlation, with six of them being significant. This research in rarely represented tram transit determined the most significant disturbance factors rich in traffic context that can be used to develop travel time prediction models.
... mobility, economy). For instance, one of the main pillars of smart cities is represented by Intelligent Public Transportation Systems (IPTS), a class of Intelligent Transportation Systems (ITS) devoted to better managing public transport [5]. In this context, the IoT and the Internet of Vehicles (IoV) [6] enable data-driven solutions, allowing the collection of both vehicle data (such as location and speed), and passenger data (such as the number of passengers boarding a public vehicle or entering/leaving a station) [7], [8]. ...
... In this context, the IoT and the Internet of Vehicles (IoV) [6] enable data-driven solutions, allowing the collection of both vehicle data (such as location and speed), and passenger data (such as the number of passengers boarding a public vehicle or entering/leaving a station) [7], [8]. These data can be used to obtain new information, on top of which it is possible to develop several use cases [9], [10], with the general goal of exploiting the available public transport resources in a smarter, more effective, and even proactive way [5], [11]. To ease the development and integration of multiple software systems for smart cities, many efforts are being made towards the definition of standards. ...
Article
Full-text available
Smart cities include complex ICT ecosystems, whose definition requires the cooperation of several software systems. Among them, Intelligent Public Transportation Systems (IPTS) aim to effectively exploit public transit resources. Still, adopting an IPTS is non-trivial. Off-the-shelf IPTS are often tied to specific technologies and, thus, not easy to integrate within existing software ecosystems. Moreover, despite IPTS introduce several peculiar issues, there is a lack of domain-specific reference architectures, which would significantly ease the work of practitioners. To fill this gap, starting from the experience gained with the Hitachi Rail company in deploying a large-scale IPTS, we identify a set of requirements for IPTS, and propose a domain-specific reference architecture, compliant with these requirements, whose primary objective is facilitating and standardizing the design of IPTS, by providing guidelines to IPTS designers. Consequently, it eases also the interoperability among different IPTSs. As an example of an IPTS obtainable from the architecture, we present a solution currently deployed by Hitachi in a major Italian city. Still, being independent from the specific considered urban scenario, the architecture can be easily instantiated in different cities with similar needs. Finally, we discuss some research challenges which should be further investigated in this domain.
... These changes aim to enhance efficiency, sustainability, accessibility and user experience. Intelligent transport systems (ITS) leverage information and communication technologies to improve the management and operation of public transport networks by real-time monitoring of vehicle locations, passenger loads and traffic conditions allowing dynamic scheduling and route optimisation [31]. Contactless payment systems and mobile ticketing apps have streamlined fare collection, reducing boarding times and improving user convenience [32]. ...
Article
Full-text available
Urban mobility (UM) refers to the movement of people and goods within urban areas. It is a multidimensional and dynamic aspect of urban life. Everyday mobility is constantly increasing. Therefore, encouraging a modal shift from private vehicles to more sustainable modes such as public passenger transport (PPT) often motivates the implementation of measures that improve service quality. Implementing improvements in public transport service quality is often expected to positively affect the demand for PPT. Therefore, quality of service (QoS) represents the basic criteria for the provided service. The European Standard EN 13816:2002 states the requirement to define, set a goal and measure the quality of service in PPT. It also provides guidelines for selecting appropriate measurement methods for determining the quality of service. Integrated passenger transport systems (IPTSs), because of their complexity and specificity (unified fare system, integrated and harmonised timetable, unified ticketing system and information system), should have different criteria levels. The current standard does not define specific criteria for IPTSs. Improving QoS criteria in IPTSs is essential for enhancing user satisfaction, making them more attractive, efficient and sustainable. This paper should determine basic QoS criteria for IPTSs.
... Controlling vehicle motion is a fundamental challenge in the study of intelligent vehicles. In this area, managing both lateral and longitudinal dynamics [1] is crucial for enabling autonomous driving. Vehicle lateral control research has mainly focused on studying the path-tracking ability of intelligent vehicles, such as controlling the steering to control the vehicle to follow a preplanned path [2]. ...
Article
Full-text available
This paper presents a scheme for the feedforward–feedback longitudinal trajectory tracking control of buses. The scheme is specifically designed to address the periodic and repetitive nature of bus operations. First, the vehicle’s longitudinal dynamics are linearized along the iterative axis via full-form dynamic linearization (FFDL), and parameters such as the pseudo-gradient are estimated with data and a projection algorithm to grasp the dynamic characteristics of the system. To better handle complex real-world traffic conditions, we then propose the forward and backward structure. At the same time, the iterative axis design performance index is verified, and the forward partial control law, namely, model-free adaptive iterative learning control (MFAILC), is derived. In order to further enhance the robustness to disturbance and other factors, the control law of the feedback part is designed with active disturbance rejection control (ADRC). A key advantage of this control approach is its sole reliance on the data generated during vehicle operation, without the need for specific information about the controlled vehicle. This feature enables the method to be adaptable to different vehicle types and resilient to various disturbances. Finally, MATLAB simulations are used to verify the practicality of the proposed method.
... The findings of this study underscore the transformative potential of AI-driven approaches in urban public transportation systems, offering significant practical implications for enhancing service regularity and operational efficiency (also analyzed in [86,87]). ...
Article
Full-text available
The functioning of modern urban environments relies heavily on the public transport system. Given spatial, economic, and sustainability criteria, public transport in larger urban areas is unrivaled. The system’s role depends on the quality of service it offers. Achieving the desired service quality requires a design that meets transport demands. This paper uses a data-driven approach to address headway deviations in public transport lines and explores ways to improve regularity during the design phase. Headway is a critical dynamic element for transport organization and passenger quality. Deviations between planned and actual headways represent disturbances. On lines with headways under 15 min, passengers typically do not consult schedules, making punctuality less crucial. Reduced headway regularity affects the average travel time, travel time uncertainty, and passenger comfort. Ideally, the public transport system operates with regular headways. However, disturbances can spread and affect subsequent departures, leading to vehicle bunching. While previous research focused on single primary disturbances, this study, with the help of AI (reinforcement learning), examines multiple primary disturbances in the cities of Belgrade, Novi Sad, and Niš. The goal is to model the cumulative impact of these disturbances on vehicle movement. By ranking parameter influences and using the automatic optimization of static line elements, this research aims to improve headway regularity and increase system resilience to disturbances. The results of this research could also be useful in developing adaptive public transport management systems that leverage AI and IoT technologies to continuously optimize headway regularity in response to real-time data, ultimately enhancing service quality and passenger satisfaction.
Article
The purpose of the research in this article is to reveal modern approaches in the development of the intelligent transport city’s networks, as a component of the social infrastructure of the modern city. The research carried out by the authors in this direction is relevant in the field regarding the improvement and attractiveness of a modern, digital city. In particular, the authors in the article examine the so-called “sleeping” districts with multi-story buildings. The authors suggest the creation of a high-speed bus transportation system to improve the services provided by public transport. The article uses the ArcGIS geoinformation system and built a geodatabase. These two components together allow you to visualize results for a visual presentation of information and qualitative analysis. The authors use such proprietary developments in the article that it is possible to model various variants of the transport network. The authors conducted research on the example of Kramatorsk, a city of regional importance in the Donetsk region. The article develops and proposes a sequence of stages in the development of a geodatabase and a transport network of high-speed bus transportation. The further direction of research may be related to the accounting of a large number of factors. The authors carried out the work with the help of the ArcGis geoinformation system and the developed geodatabase with the necessary layers and attribute information. The article develops a system of high-speed bus transportation in the city of Kramatorsk with promising routes. Such development will allow further improvement of the social infrastructure of other cities. The principle of preliminary modeling using the developed geodatabase used by the authors will be effective for solving issues related to social infrastructure, so it can be applied to solve similar problems in any city. Thus, the authors’ intellectual analysis of geostatic models of public transport movement on the territory of the city made it possible to find zones and classify them depending on the pedestrian accessibility to stops and compare them with existing transport routes. With the help of geostatistics methods, the article transforms data from a discrete to a continuous form of representation.
Article
Full-text available
This paper describes a vehicle routing and scheduling system that combi-nes Geographical Information Systems (GIS) with stochastic programming models that incorporate travel time variability information. GIS provide a framework for integrating traffic networks and performance data allowing a realistic representation of the traffic network to be considered. Stochastic programming allows the variable nature of vehicle travel times to be incor-porated within routing and scheduling procedures. New automated data col-lection technology allows information on the variability of travel times in urban areas to be obtained easily. The integrated approach presented in this paper has the potential to substantially reduce the costs of distributing goods in urban areas.
Conference Paper
Full-text available
In our days, it become difficult to offer high quality services, in term of punctuality, frequency and productivity with complex and unpredictable disturbances that affect public transportation schedules. The development of a regulation support system to help human operators becomes a necessity. In this paper a real time regulation support system for public transport regulation based on human immune theory is presented. Artificial immune system presents many interesting capabilities of identification, learning, memory and distributed parallel processing. This approach has a good performance in terms of convergence and quality of solutions which are proven and shown through tests and results.
Article
Full-text available
The public-transportation sector is in constant development, yet the operating companies need to improve the quality of service they provide to passengers. In order to achieve this goal, they have to increase passenger security, comfort, information access, the destinations offered and extended service schedules. These improvements are very expensive to any public-transportation operator; the companies’ sustainability will be compromised. This paper describes an Intelligent Transportation System (ITS) that allows public-transportation companies to implement Real-Time Information to Passenger System (RTPI), that also provides monitoring and management tools to optimize service management and administration. The SITREPA System includes a hardware and software combined system that acquires data from vehicles and provides information to the needs of different actors in the public-transportation environment. We have designed a system that with low complexity and with an affordable cost, adequate for most small and medium passenger operator enterprises. The result of the implemented prototype in test scenarios showed that this system can be an important tool to passengers and can improve public-transportation management services.
Article
Full-text available
In order to improve travel times for public surface transport vehicles (buses, trams, etc...), urban traffic control systems are used to give priority to public transport vehicles. The main target of these strategies is to reduce the delay when crossing an intersection, for all private and public transport vehicles. However, they do not take into account buses regularity. Our objectives in this research are to regulate urban traffic but also to ensure the regularity of buses. To do, we develop a multi-agent bimodal urban traffic control strategy. This multi-agent strategy acts on traffic lights to regulate traffic, promote passage of buses, while monitoring the intervals between buses driven on a given bus route. We present a model adapted to a real traffic situation and propose new alternatives to better regulate the bimodal urban traffic between public transport (buses) and private cars.
Article
To ensure better regulation, the emergence of Support Systems Operations (SSO) had helped the regulators. SSO allow real-time monitoring of operating a urban transport network. We consider the problem of matching supply with actual operating conditions. To do this, we propose a system for decision support that provides traffic monitoring, incident detection, diagnosis and control. This system uses two concepts of artificial intelligence: The multi-agent systems and the anytime approach. We present a model of multi-agent decision support in real time. We rely on a new approach based on progressive reasoning, to take into account real-time in a MAS: the proposed model is called SMAST (Multi-Agent System for Modeling Transport Systems). It provides users a rich environment for modeling tools and techniques according to the Geographic Information Systems GIS. Algiers city was taken as a study case. It is a very complex case for intermodal synchronization, because it includes various modes of transport. (Bus, urban train, metro, tram).
Article
Dynamic vehicle dispatching at the transfer station can improve the transit service quality by optimizing the transfer coordination of routes. In this paper, a dynamic vehicle dispatching model is proposed that aims to minimize the total waiting time of passengers at the transfer station and the downstream stops. A prediction model based on support vector machines (SVM) is also developed to forecast the arrival time of the next vehicles at the transfer station. To reduce the disconnected cases between the transfer routes, an SVM-based model is introduced to forecast the elastic time for the estimated arrival time at the transfer station. According to the estimated arrival time and elastic time, vehicles are dispatched in a dynamic way to reduce the total waiting time of passengers. The dynamic vehicle dispatching approach at the transfer station is examined with the data of three transfer routes in the city of Dalian, China. Results show that the approach proposed in this paper can reduce the total waiting time of passengers at the transfer station and the downstream stops. DOI: 10.1061/(ASCE)TE.19435436.0000311. (C) 2012 American Society of Civil Engineers.
Conference Paper
Intelligent public transportation system is an effective way to improve the quality of our country transportation. Wireless sensor network is a novel technology made by the convergence of sensor technology, micro electro mechanism technology, wireless telecommunication technology and network technology. It is suitable to transportation scenarios for the characteristic of rapidly deployed and self organized. An improved solution combined the wireless sensor network and internet technology is presented in the article. It effectively solves three critical problems of the wireless sensor network, including energy saving, localization and communication distance. By using the low cost and high stability microchip, a high reliability and low cost intelligent public transportation system based on wireless sensor network can be easily established.
Conference Paper
Mobile platforms are now becoming a more and more important medium for providing information to travelers on the move. To improve the accuracy and relevance of the mobile traveler information, context awareness has become a active research topic. In this paper, we describe algorithms and services provided by Path2Go, a multimodal traveler information system developed by California PATH, UC Berkeley. The Path2Go activity detection algorithm, the core of the context-aware design, has made the following contributions: (1) it uses a rule-based multi-hypothesis Bayesian method for mode detection, to address the limitations of using mobile phone GPS for activity detection and increase the speed of convergence; (2) it fuses GPS data from transit vehicles and the GPS of the user's mobile phone for better activity detection; and (3) it enables several experimental services, including variable-frequency client-server communication and need-based GPS use. Field testing of the Path2Go activity detection algorithms showed reasonably good results. The Path2Go application has also been made available to the public through iPhone, Android and Windows Mobile platforms, with some of the features discussed in this paper included.
Article
This paper introduces a system design about bus management system based on ZigBee and GSM/GPRS, which implemented the basic functions of the intelligent public transport management system, such as monitoring the time of bus arrival, departing from the bus station and reporting stations name automatically. This system can ensure punctuality of vehicles to run, improve the automation level of reporting stations and quality of public transport service. The management system has low cost and thus it is more feasible.
Article
A new transit operating strategy is presented in which service vehicles operate in pairs with the lead vehicle providing an all-stop local service and the following vehicle being allowed to skip some stops as an express service. The underlying scheduling problem is formulated as a nonlinear integer programming problem with the objective of minimizing the total costs for both operators and passengers. A sensitivity analysis using a real-life example is performed to identify the conditions under which the proposed operating strategy is most advantageous.