Engineering, 2010, 2, 270-289
doi:10.4236/eng.2010.24037 Published Online April 2010 (http://www.
Copyright © 2010 SciRes. ENG
A Framework for Intelligent Decision Support System for
Traffic Congestion Management System
Mohamad K. Hasan
Department of Quantitative Methods and Information Systems
College of Business Administration, Kuwait University, Kuwait city, Kuwait
Received November 18, 2009; revised January 22, 2010; accepted February 4, 2010
Traffic congestion problem is one of the major problems that face many transportation decision makers for
urban areas. The problem has many impacts on social, economical and development aspects of urban areas.
Hence the solution to this problem is not straight forward. It requires a lot of effort, expertise, time and cost
that sometime are not available. Most of the existing transportation planning software, specially the most
advanced ones, requires personnel with lots practical transportation planning experience and with high level
of education and training. In this paper we propose a comprehensive framework for an Intelligent Decision
Support System (IDSS) for Traffic Congestion Management System that utilizes a state of the art transporta-
tion network equilibrium modeling and providing an easy to use GIS-based interaction environment. The
developed IDSS reduces the dependability on the expertise and level of education of the transportation plan-
ners, transportation engineers, or any transportation decision makers.
Keywords: Traffic Congestion Management System; Transportation System Management; Intelligent
Decision Support System; Urban Transportation Systems Analysis; Multiclass Simultaneous Transportation
Equilibrium Models; Intelligent Scenario Creation Assistance Agent
1. Introduction
Over the last few decades the dimensions of the trans-
portation system and the interaction between these di-
mensions and the socioeconomic system, have increased
by many folds. And despite the fact that significant ad-
vancements have occurred in Information and Commu-
nication Technologies (ICT)-based transportation sys-
tems and many network equilibrium models have been
developed, comprehending this dynamic environment,
identifying its critical issues, and responding to them by
selecting the right alternative at the right time is still an
immensely demanding and difficult task to accomplish
even for well trained and experienced transportation ex-
Urban transport problem can be understood in relation
to land use and other socio-economic activities which the
transportation network serves. A relationship exists be-
tween trip frequencies and socioeconomic characterris-
tics of trip makers and the purpose for which the trip is
made (such as journey to work, to school, to shopping, to
social activities, etc. Each of these types of trips is spa-
tially distributed over the city between the residential
areas and places of interest and activities. Trip purposes,
also influence the volume of trip generation in various
land use in the city [1]. This is because the volume of
journeys made from origin to destination is functionally
related to the ability of respective areas to generate or
attract movement. Reasons such as inadequate transport
infrastructure, rapid increase in urban population, income
and automobile ownership etc., had been noted to con-
tribute to traffic congestion in many of the urban areas
[2]. Traffic congestion is a critical problem in urban ar-
eas. It is not always fruitful to expand the transportation
systems, if the demand is not satisfactorily met by the
existing capacity. As such it is prudent to optimize the
use of existing transportation system by certain man-
agement techniques.
Gazis [3] proposed a way to control closely spaced
and oversaturated intersections. Gordon [4] suggested
that an intersection control scheme should be aimed at
controlling the relative queue lengths on the phases.
Longley [5] identified two types of congestion and pro-
posed a signal control and queue management procedure
that aims at reducing secondary congestion. Pignataro et
al. [6] described a queue management procedure. Gartner
M. K. HASAN.271
et al. [7] presented a procedure with multi-level design
for real time, traffic-adaptive control. Other work on
queue management appeared in the literature [8-10].
Reddy et al. [11], Reddy [12], described the Transporta-
tion System Management (TSM) actions to improve ve-
hicular movements with the help of a case study. Thom-
son [13] described the traffic management problem faced
by highly urbanized cities of developing world due to the
rapid urbanization relating to traffic congestion. This
effort at reducing traffic congestion without providing
for more road capacity is expected to shift the trip pattern
of motorists in various ways, notably to redistribute trips
spatially, temporarily, and modally. Other studies on
urban traffic management emanating from non commis-
sioned studies include that of Adefolalu [14], on traffic
congestion in the city of Lagos; Oyefesobi [15] on acci-
dent reduction: Orioke [16], on traffic education: Ogun-
bondede [1], on the contribution of land use to traffic
These above studies show that traffic congestion
problem is a common phenomenon in urban and part of
TSM actions to improve the existing transportation sys-
tem for the better serviceability and efficiency by taking
various measures. Transportation Systems Management
(TSM) measures, originally conceived as a tool applica-
ble on a region-wide scale, can be successfully applied at
major activity centers to avoid, minimize or postpone the
need for more capital-intensive transportation improve-
ments [17]. A transportation management system (TSM)
is intended to provide information on transportation sys-
tem performance and identify alternative actions to alle-
viate congested roadway conditions. Although a TSM
can use data obtained from a traffic operation surveil-
lance and control system, in general a TSM is considered
to be more a planning tool than an operations tool [18].
The main objectives of TSM actions are to coordinate
all individual elements of transportation systems through
operating, regulatory and control policies, so as to achieve
the maximum efficiency and productivity and minimize
environmental pollution and energy consumption. TSM
actions, whether applied on a corridor or at city network
level, aim at maximizing the expected output of the
transportation systems as whole and achieve the specific
objectives [11].
TSM actions include:
Assuring the safety and the efficiency flow of pas-
senger vehicles and trucks along urban and rural
transportation networks [19].
Choosing the most efficient improving alterna-
tives solutions of the existing transportation system
problem at a minimum cost [19].
Increasing the transit system ridership by increasing
the frequency of buses or trains, reducing the fair,
and assuring scheduling time accuracy.
Minimizing the air pollution, noisy, and all others
undesirable environmental impacts of the existing
transportation system [19]
Banning of particular class of vehicles at certain
times/certain areas.
Auto free zones where vehicles are totally prohib-
Special bus lanes and streets where spaces are re-
served for public buses and emergency vehicles and
other improvements of facilities for buses viz. bus
bays and bus stops.
Fiscal measures like parking fee, road pricing and
supplementary licensing etc. which in turn make the
less vehicle usage.
Parking Control
Staggering of office hours
Car pooling
Travel Bonus
Controlled Entry
Banned turns
Geometric improvements
Improvements for slow moving traffic
Improvements to pedestrian facilities
Improvements in intersection circulation
Synchronization and redesign of signals
Detailed benefits of the TSM Program can be found
in [19].
To evolve TSM action plan for the selected corridor
there is a need to understand and analyze the characteris-
tics of traffic flow as well as existing transit system, land
use development which generates the traffic on this cor-
ridor and their ill effects.
To integrate the entire above feature of TSM, we first
defined Congestion Management System (CMS) and
Intelligent Decision Support System (IDSS).
A Congestion Management System (CMS) is a sys-
tematic process of monitoring, measuring, and diagnos-
ing the causes of current and future congestion in major
travel corridors [20]; performing a detailed evaluation
analysis and recommendations for different alternatives
solutions strategies that can be implemented to improve
management of current and future congestion [20]; and
monitoring and evaluating the performance of strategies
that have been implemented to manage or mitigate con-
gestion [21]. US Federal transportation legislation re-
quires Metropolitan Planning Organizations (MPO) to
develop and implement a CMS as part of the metropoli-
tan transportation planning process [22]. The CMS pro-
vides a consistent basis to make transportation invest-
ment decisions and ensures that travel demand manage-
ment (TDM) and transportation system management
(TSM) measures are considered prior to roadway capac-
ity expansion, in accordance with goals and objectives
established by any Metropolitan Planning Organization
[20]. The MPO is a transportation policy-making organi-
zation made up of representatives from local government
and transportation authorities.
The dynamic and complex nature of transportation en-
Copyright © 2010 SciRes. ENG
vironment, the complexity of the current transportation
system and difficulty to comprehend the network equi-
librium models and the limited availability of transporta-
tion expertise together created an extremely hard envi-
ronment to deal with efficiently and effectively. How-
ever, the advancement and availability of intelligent
agent-based technologies and solutions for integration
within a comprehensive transportation system analysis
framework, as a Traffic Congestion Management System
(TCMS), provide promising solutions.
Ossowski et al. [23] summarized good literatures ab-
out the Decision Support Systems that include previous
works of French [24], Silver [25], Klein and Methlie [26],
Hernandez and Serrano [27], Hernandez and Serrano
[28], Vlahavas et al. [29], Cuena and Ossowski [30],
Ossowski et al. [31], Iglesias et al. [32], and Hoa Dam
and Winikoff [33].
More details about the decision support system fun-
damentals and the classification of decision support sys-
tems can be found in Holsapple [34].
Recently, Zhang [35] addressed the requirements for
an open and standardized environment for integrating
various DSSs. He also addressed the emergence of intel-
ligent agent technology to fulfill the requirements of de-
veloping innovative and efficient DSS applications. In
his thesis he gave detailed illustrations of the intelligent
agents in offering various advantages, such as mobility,
flexibility, intelligence, etc., to overcome the major pro-
blems in existing DSSs.
Creating an intelligent agent-based DSS for TCMS
that would enable and assist the transportation system
users (operators) to identify critical transportation prob-
lem issues and to respond to them by selecting the right
course of action in a timely, efficient and effective man-
ner would be a response to this critical requirement. To
this end, the objective of this paper is to provide such a
The main objective of this research paper is to develop
A Framework for Intelligent Decision Support System
for Traffic Congestion Management System, using state
of the art developments in urban transportation system
modeling, that can be a useful decision support tool for
transportation planners and decision makers for the
analysis and evaluation of strategic transportation plans
include transportation projects and policies.
The main contribution of this research is the creation
of Scenario Management Subsystem (SMS) and the In-
telligent Scenario Creation Assistance Agent (ISCAA)
briefly discussed in Section 4.5 and Section 4.6 respec-
tively. We will also show how these components are
integrated with the impact models results where the in-
puts to these impact models are the result of the Multi-
class Simultaneous Transportation Equilibrium Model
(MSTEM) [36] that will be run for each transportation
alternative solution. And finally, we have an intelligent
DSS that can recognize a pattern of solution alternatives
for traffic congestion problem associated with its total
evaluation. This IDSS can be used by any non-
transportation specialist decision makers for any trans-
portation policy evaluations and recommendations.
This paper is organized as follow: Section 2 gives the
main concept for the transportation systems analysis.
Section 3 presents a more detailed framework for trans-
portation system analysis combing the concept of the
transportation planning process and the transportation
system analysis concept. Section 3 presents The Multi-
class Simultaneous Transportation Equilibrium Model
(MSTEM)’s description, formulation as Variational Ine-
quality (VI) and the data collection requirements. Section
4 illustrates the main Architecture of the Intelligent De-
cision Support System (IDSS) implementing the frame-
work for transportation system analysis described in Sec-
tion 2. And finally, the summary, conclusion and future
research are presented in Section 5.
2. Transportation Systems Analysis
2.1. Transportation System
We briefly define the transportation planning as a con-
tinuous process consists of:
1) Problem definition, Generation of Alternatives
2) Building and Calibration of Transportation Models
3) Application of the Calibrated Transportation Mod-
els and Analysis of Alternatives
4) Evaluation and Choice
5) Implementation
A transportation system can be define as the combina-
tion of elements and their interactions, which produce the
demand for travel within a given area and the supply of
the transportation service to satisfy this demand. This
definition is general and flexible enough to be applied to
different context. The specific structure of the system is
defined by the problem itself (or class of problem) for
whose solution is employed.
Almost all of the components of a social and economic
system in a given geographical area interact with diff-
erent levels of intensity. However, it is practically im-
possible to take into account every interacting element to
solve a transportation problem. The typical system ap-
proach is to isolate the most relevant element in the
problem. These elements, and the relation among them,
make up the analysis system. The remaining elements
which belong to the external environment are taken into
account only in term of their interaction with the analysis
system. The transportation system of a given area can
also be seen as a sub-system of a wider territorial system
with which it strongly interacts. The extent, to which
these interactions are included in the analysis system, or
in the external environment, depends on the specific
Copyright © 2010 SciRes. ENG
M. K. HASAN.273
Transportation is the process of transferring people,
good and information from one place to another. To per-
form its function, any transportation system consists of
several components which together act as a single unit
that is designed and developed to provide a suitable
technology for the “objects” to be transported.
Any transport technology must provide mobility, con-
trol, protection, and land access for the objects. Perhaps
the most widespread transport technology is the one used
for inland transport. That is vehicles and containers op-
erating on highway or railway networks.
Components of such a transport system may be di-
vided into two categories. The first category includes the
physical components such as the network infrastructural
fixed elements (i.e., road and rail links, intersections,
terminals, parking spaces, railway yards, maintenance
shops, stations, etc.), and moving elements (i.e., vehicles
and containers). The second category includes the human
components such as users, operators, owners, and regu-
lators of the system, government and the society at large.
The key players in system analysis are defined as fol-
1) Users: are those traveler and shipper who represent
demand on the transport system.
2) Operators: are those who own the fleet of vehicles
and hence, are responsible for their operation, mainte-
nance and investment.
3) Owners: are those who own the network elements
and hence, are concerned with construction, upgrading,
operation and maintenance of the infrastructure.
4) Regulators: are responsible for traffic laws, ordi-
nances and regulations.
5) Government: is responsible for creating the master
plan for the city transportation systems and policies or
projects that affect the transportation systems.
6) Society at large: is responsible location of residents
and activities
It should be emphasized that these components should
interact all together in order to provide effective and ef-
ficient “transportation”. Furthermore, a transport system
may be viewed as one of several components of more
complex socio-economic system of the society. The inter-
action between the transport system and its surrounding
socio-economic environment is, again, evident. Transport
demand is a function of the magnitude and spatial distri-
bution of socio-economic activities which in turn are
greatly influenced by the characteristics of transport sys-
tems. Therefore, the actual performance of any transport
system is a function of several interacting and interde-
pendent factors within the system and those outside the
Like any other complex system, transport system may
not always perform as desired and there are often prob-
lems and issues to be addressed and resolved. Traffic
congestion, limited parking space, high accident rate,
weak connectivity between major development centers,
freight movements, public transit, and air pollution are
but a few examples to mention. The range of possible
remedies is enormous. Construction of new highways,
building multistory garages, introducing new transport
technologies, creating new organizational structures and
traffic regulations are examples of actions which would
be undertaken.
The “best” action (or set of actions) to be implemented
in particular situation, is a question that is, often, not a
simple one to resolve immediately and usually requires a
systematic process of analysis that takes into considera-
tion the interacting affects on the system, that is, trans-
portation planning. Formally defined, planning is “a sys-
tematic analytical process that assists decision makers of
a given system to achieve a specific set of goals and ob-
jectives within a given socio-economic environment in
an optimum fashion”.
2.2. Basic Premises
As indicated earlier any transport system consists of sev-
eral physical as well as human components which inter-
act together in order to produce “transportation”. The
transport system itself is, again, one of several compo-
nents in the socio-economic activity system of the soci-
ety when all interact produce what we call the “devel-
opment” of that society. Therefore, the analysis of trans-
portation systems should be based on two basic prem-
1) The total transport system within a given socio-
economic environment must be considered and viewed
as a single multimodal system.
2) The interaction between the total transport system
and the surrounding socio-economic activity system
must be taken into account in the analysis.
2.3. Basic Variables and Relationships
Based on the above premises, the resultant effect of the
interactions between the activity and transport systems is
manifested in the flow pattern distributed on the different
elements of the transport system. Therefore, we can de-
fine three basic variables for the analysis:
T: The Transport System,
A: The Activity System
F: The Flow Pattern
The interrelationships among these three variables are
shown on Figure 1 and may be described as follows:
Relationship I: T and A determines F
Relationship II: F cause changes over time in A (e.g.,
an increase of flow on a given route may induce more
activities to shift along that route).
Relationship III: F causes changes over time in T (e.g.,
a congestion of flow may influence the decision to build
a new road or to modify existing one).
Copyright © 2010 SciRes. ENG
Figure 1. Basic variables and relationships in transporta-
tion system analysis.
In order to complete the description of the basic
framework of the analysis it is essential to identify the
major individuals, groups or institutions whose decision
could influence and change any of the three basic vari-
ables of the analysis T, A and F. Six major groups can be
identified: User, operators, owners, regulators, govern-
ment, and society at large (a brief description of each
group has been introduces earlier).
2.4. Basic Issues
Having defined the basic framework of analysis let us
turn our attention to the basic issues involved. The first
basic issue is related to the available “options” through
which the six major groups can influence the system.
The second basic issue is concerned with predicting the
possible “impacts” of these “options” on the same groups.
Thus the analysis can be described by firstly identifying
the available options, secondly identifying the possible
impacts, and thirdly explaining the process of predicting
the set of impacts resulting from a given set of options.
2.5. Options
Options or “decision variables” are those aspects of the
transport and activity systems which can directly be
changed by the decision(s) of one or more individuals or
groups. It is, therefore, natural to divide the available
options into two categories, the first includes those re-
lated to the transport system and the second includes
those related to the activity system.
1) Transportation options:
Transportation options are those decisions which can
mainly influence the transport system’s performance by
changing aspects of the network, technology, operating
policies, and/or institutional structures as follows:
a) The network may be influenced (by the owners)
through the geometric and structural design of different
links, intersections and terminals, the network topology
and hierarchy, traffic signals, signs, markings, parking
facilities, etc.
b) Technological options may include the use of elec-
tric or solar power for vehicles, the use of containers, the
introduction of a new rapid transit system, etc.
c) Operating policies may include vehicle routing,
scheduling, pricing, exit and entry regulations, financial
regulations, laws, ordinances, etc.
d) Institutional options may include the number and
types of institutions, the functions of different instit-
utions, the domain of responsibilities, channels of com-
munications, coordination, control, etc.
2) Activity options:
Activity options are those decisions which can mainly
influence transport demand and which are, in general,
not controlled by the decision makers of the transport
system. People in the society have a wide range of op-
tions about how, when and where they would conduct
their activities. Two types of decision should be consid-
a) long term decisions:
-The location of residence
-Scale and pattern of activities
These decisions determine the spatial distribution of
socio-economic activities and land uses in a given area.
Within this context, the actual transport demand will be
influenced by
b) short term decisions (travel options):
-Trip purpose
-Time of trips
-Frequency of trips
-Trip mode
-Trip route
2.6. Impacts
Impacts are those aspects of the transportation and activ-
ity systems that would be influenced by the implementa-
tion of alternative options and would consequently in-
fluenced the six major groups of the system: users, op-
erators, owners, regulators, government, and the society
at large. In order to predict and evaluate these impacts it
is useful to categorize them according to the affected
1) User impacts
Users are mainly influenced by the level of service of
the transport system, and hence, their impacts variables
would include:
- Travel time
- Travel cost
- Safety
- Comfort
2) Operators impacts
Operators are mainly concerned with maximizing their
share of demand and/or their net revenues, and hence,
their impact variables would include:
- The costs of several resources consumed in the op-
Copyright © 2010 SciRes. ENG
M. K. HASAN.275
- Maintenance and investment of their vehicle fleets
- Their shares of transport demands
3) Owners impacts
Owners are mainly interested in the usage and condi-
tion to their infrastructural elements, and hence their im-
pacts would be reflected through the investment, main-
tenance and upgrading costs which are, in general, func-
tions of the usage of the transport network.
4) Regulators impacts
Regulators are mainly interested in the safety aspects
of operation.
5) Society impacts
The impacts on the society at large include the effects
caused by the physical presence of transport facilities,
such as:
- Noise
- Pollution
- Relocation of residents and activities
6) Governmental impacts
Governmental impacts will depend upon whether the
given government institution is a user, operator, owner,
regulator or representing the society at large.
It should be obvious that the above impacts will, in
general, have differential effects on different groups, or
even subgroups of the above six major ones. That is,
some will gain while others may lose.
2.7. Prediction Process
As indicated earlier, the core of the analysis is the proc-
ess of predicting the “impacts” of alternative sets of “op-
tions”. A central aspect of this process is the prediction
of an “equilibrium” flow pattern on the transport system
resulting from a given set of options. Once this equilib-
rium flow is predicted, the different impacts variables
can be estimated through a set of impact models. The
prediction process may, then, be described as follows:
1) Specification of the transport system options T es-
tablishes performance functions, J. The function J indi-
cates how the level of service S varies as a function of
the transport options T and the volume of flow V. That is,
S = J (T,V)
2) Specification of the activity system options A estab-
lishes demand function, D. The function D indicates the
volume of flow V as a function of the activity options A
and the level of service S, that is,
V = D (A, S)
3) The flow pattern F consists of the volume Vand the
level of service S on the different elements of the total
transport system. That is,
F = (V, S)
4) For a given set of options T* and A*, the resulting
equilibrium flow pattern, F*, can be computed by solv-
ing the performance and demand function. That is,
(*, )*( *,*)
3. The Methodological Framework for
Urban Transportation Systems Analysis
Figure 2 shows the main components of the method-
ological framework for urban transportation systems ana-
lysis as a Traffic Congestion Management System (TCMS)
that we will develop in this research paper and which
depends on the transportation planning steps described
3.1. An Overview
This framework can be described as follow:
1) Socio-Economic Environment Description
This part of the frameworks represents the diagnoses
and the analysis of the socio-economic environment
characteristics and factors of the urban area that should
be considered in the analysis and developments of the
transportation system. These factors may include the
population distribution, income, car ownership, and land
use of the study area. In this part, we can also define the
transportation problems that should be addressed and
clearly and specifically determined the symptoms, cause,
and consequences for each problem in addition to the
objectives and goals that should be satisfied by solving
these problems. Then different alternative can be gener-
ated through different options of the six groups’ users,
operators, owners, regulator, government, and society at
large as in next parts (b) and (c). A related data collec-
tion should be performed in this part.
2) Users Characteristics and Behavior
This part represents the transportation demand side
where there are, for example, the following alternatives
(options or decision variables):
- Residence Locations
- Economic& Social Activities Locations
- Frequency of trip
- Destination of Trip
- Trip Purpose
- Travel Mode
-Time of Trip
- Work Locations
- Trip Route
3) Owners, Operators and Regulators Behavior
This part represents the transportation system per-
formance side where there are, for example, the follow-
ing alternatives (options or decision variables):
- Network Configuration (e.g., links, nodes, intersec-
tion, traffic signals location, waiting areas)
- Investments and maintenance Policies for the trans-
portation network and facilities
Copyright © 2010 SciRes. ENG
- Vehicle fleets characteristics
- Routing, scheduling and pricing policies
- Laws, regulations and controls
- Institutions
4) Transport Demand
As a result of a combination of alternatives (options) of
users characteristics and behavioral in part (b), a set trans-
port demand functions (in fact a set of transportation de-
mand models) can be defined. These demand functions will
be functions of user behavior and system’s performance.
5) Transportation System Performance
As a result of a combination of alternatives (options)
of owners, operators and regulators behavior in part (c), a
set transport performance functions (in fact a set of
transportation performance models) can be defined.
These performance functions will be functions of trans-
portation supply and transportation demand. A combina-
tion of alternatives from part (b) and part (c) together is
considered a complete scenario (alternative) that would
developed (generated) to solve the problem under con-
sideration in part (a). This is considered as step (b) in
transportation planning steps. Parts (d) and (e) required
building transportation demand and performance models
and calibrating these models using the data collected in
part (a). This is considered as part of step (c) in transpor-
tation planning steps.
6) Transportation Network Equilibrium
To analysis any completed scenario (alternative) gen-
erated by part (b) and (c), an equilibrium between the
built and calibrated transportation demand models (V =
D(A,S)) and transportation performance models (S = J
(T,V) should be performed. The output of this equilib-
rium process is the traffic pattern (F = (V,S)) that can be
used in estimate the impacts of the given scenario on
users, owners, operators, regulators, and society at large
(see part (g) next). Part (d) represents, in general, the
activity system Awhile part (e) represents, in general, the
transportation system T at interacting together to produce
the traffic pattern F. This represents Relation I in Figure 2.
This part represents part of step (c) in transportation
planning steps. To compute this equilibrium we will ex-
plain in the next subsection in detail our MSTEM model
7) Impacts on Users, Owners, Operators, Regulators,
Government, and Society at large
The traffic pattern result from part (f) can be used in
different impact models that should be developed for the
six groups: Users, owners, operators, regulators, gover-
nment, and society at large (see Section 1.3 part (e)).
This completes step (c) in transportation planning steps.
8) Evaluation and Choice
As a result of the impacts on the six groups in part (g),
each alternative can be evaluated using specified eval-
uation criteria and the best alternative (scenario) should
be chosen. (Step (d) in transportation planning steps)
9) Implementation
It is same as step (e) in transportation planning steps.
The traffic pattern F that used in the alternatives eval-
uation, choice and implementation will cause chan- ges-
over time in activity system, A, through part (b), this
represents Relation II in Figure 1. This traffic pattern
will also cause changes over time in transportation sys-
tem, T, through part (c), this represents Relation III in
Figure 1.
3.2. The Multiclass Simultaneous Transportation
Equilibrium Model (MSTEM)
Single class travel forecasting models assume that all
travelers are similar in their travel-decision characteris-
tics, such as their money-value of the time and their sen-
sitivity to travel times in choosing their origin, destina-
tion and mode of travel, etc. To obtain more realistic
models, travelers are often divided into classes, either by
socio-economic attributes (e.g., income level, car avail-
ability, etc.) or by the purpose (e.g., home-based-work,
non-home-based-work, home-based-shopping, etc.) of
their travel, assuming that travel-decision characteristics
are the same within each class, but differ among classes.
However, the development of this concept of multiple
classes increases the mathematical complexity of travel
forecasting models. In 1986 researchers in Chile began to
implement multiclass combined models emphasizing
route choices in a congested transit network with several
combinations of transit modes, as found in Santiago [37].
This research led to the development of ESTRAUS and
related software, which has been extensively applied to
Santiago as well as other Chilean cities. Florian, Wu and
He [38] proposed a variant of ESTRAUS intended to be
more efficient computationally.
All existing multiclass combined models including
ESTRAUS, consider that the total originating and termi-
nating flows are known, i.e., the trip generation step of
transportation planning process is exogenous to the com-
bined prediction process. This deficiency is accounted in
the STEM model which is the only model that combined
the trip generation in the prediction process, but it is not
a multiclass model. This encourage Hasan and Dashti [36]
to expand STEM model to be multiple user classes model
in terms of socio-economic group, trip purpose, as well
as pure and combined transportation modes, interacting
over a physically unique network. The developed Multi-
class Simultaneous Transportation Equilibrium Model
(MSTEM) will also combine explicitly the departure
time as one of the main components of the prediction
process for the first time and will be considered as a new
generation of new Case 6 of the multiclass model classi-
fication of Boyce and Bar-Gera [39]. The MSTEM will
include all the features of ESTRAUS in addition to the
other features mentioned above and more flexible struc-
Copyright © 2010 SciRes. ENG
Copyright © 2010 SciRes. ENG
ing given by an entropy maximization model in ES-
TRAUS), and modal split and departure time are given
by Multinomial Logit (MNL) models based on the ran-
dom utility theory (instead of hierarchical Logit for mo-
dal split only in ESTRAUS). The MSTEM is formulated
as a Variational Inequality problem and a diagonalization
(relaxation) algorithm is proposed to solve it.
ture for demand models where the trip generation can
depend upon the system’s performance through an ac-
cessibility measure that is based on the random utility
theory of users’ behavior (instead of being fixed as in
ESTRAUS), trip distribution is given by a more behav-
iorally richer Multinomial Logit (MNL) model based on
the random utility theory (instead of being given by an
entropy maximization model in ESTRAUS), and modal
split and departure time are given by Multinomial Logit
(MNL) models based on the random utility theory (in-
stead of hierarchical Logit for modal split only in ES-
TRAUS). The MSTEM is formulated as a Variational
Inequality problem and a diagonalization (relaxation)
algorithm is proposed to solve it. The MSTEM will in-
clude all the features of ESTRAUS in performance thr-
ough an accessibility measure that is based on the ran-
dom utility theory of users’ behavior (instead of being
fixed as in ESTRAUS), trip distribution is given by a
more behaviorally richer Multinomial Logit (MNL)
model based on the random utility theory (instead of be-
3.2.1. MSTEM Modeling Notation
Let G = (N, A)be a multimodal network consisting of a
set of nodes and a set of A links that can represent
any mode of transport m in an urban area. These modes
can be grouped into different nests n that could be multi-
ple pure and combined (combination of pure) modes. A
typical user of class l with trip purpose traveling
from a given origin i at a specific departure time period
to any destination j that is accessible from i can use
any of these modes for his journey. We will use the fol-
lowing notation for the multiclass models:
Figure 2. A framework for urban transportation system analysis as a TCMS.
G = (N,A) A multimodal network consisting of a set of
nodes and a set of A links N
L = User class (e.g., income level, car availability, etc.)
L = Set of all user classes
O = Trip purpose (e.g., home-based-work, home-ba-
sed-shopping, etc.)
O = Set of all trip purpose
I lo = Set of origin nodes for user class l and trip pur-
pose o
i = An origin node in the set I lo for user class l with
trip purpose o
D= Set of destination nodes that are accessible from
a given origin i for user class l with trip purpose o
j = A destination node in the set for user class
with trip purpose o
Rlo = Set of origin-destination pairs ij for user class l
with trip purpose o, i.e., the set of all origins iI lo and
destinations j
M = Any transportation mode in the urban area
n= Nest of transportation modes that has a spe-
cific characteristics (e.g., pure modes including private
and public or combined modes) that are available for
user class l with trip purpose o travel between ori-
gin-destination pairs
= Set of all nests of modes n that are available for
user class l with trip purpose o travel between ori-
gin-destination pairs ij
= Set of all transportation modes in the nest
for user class l with trip purpose o travel between
origin-destination pairs ij
t= Departure time period for user class with trip
purpose o using mode m in the nest n to travel between
origin-destination pairs ij
= Time horizon of the departure time periods
for users of class l with trip purpose o using mode m be-
tween origin-destination pairs ij
p= A simple (i.e., no node repeated) multimodal path
(i.e., it may include links with combined modes m) in the
multimodal network(, )NA
= Set of simple paths for travel from the origin
node i to destination node j in the multimodal net-
work for users of class l with trip purpose o de-
part at time using mode from the nest
of modes .
a = A link in the set A in the multimodal
u= the perceived minimum (generalized) cost of
travel for users of class l with trip purpose o depart at
time t using mode m in the nest n from the origin node i
to destination node j in the set
S= the accessibility of origin as perceived
from user of class l with trip purpose o traveling from
that origin
G= the number of trips generated from origin i for
users of class l with trip purpose o
= the value of the socio-economic variable
that influences trip attraction at destination for users
of class l with trip purpose o
lo lo
gA = a given function specifying how the
socio-economic variable
influences trip attraction
at destination j for users of class l with trip purpose o,
= a composite measure of the effect that socio-
economic variables, which, are exogenous to the trans-
port system, have on trip attraction at destination j for
users of class l with trip purpose o.
and lo
for 1, 2, ..., wW
are coefficients to
be estimated, where . 0
= the value of the th
socio-economic variable
that influences the number of trips generated from origin
i for users of class l with trip purpose o
= a given function specifying how the th
socio-economic variable,lo
, influences the number of
trips generated from origin i for users of class l with trip
purpose o, and
E= a composite measure of the effect the socio-
economic variables, which are exogenous to the transport
system, have on the number of trips generated from ori-
gin i for users of class l with trip purpose o
for 1, 2, ...,
are coefficients to
be estimated lL
= the number trips of users of class l with trip
purpose otraveling from the origin node to the
destination node and whose already chose the
mode of transport from the nest of modes
and start their trip at the time
= the number trips of users of class l with trip
purpose otraveling from the origin node to the
destination node and whose already chose the
mode of transport from the nest of modes
T= the number trips of users of class l with trip
purpose otraveling from the origin node to the
destination node and whose already chose the
Copyright © 2010 SciRes. ENG
M. K. HASAN.279
nest of modes .
Km 
T= the number trips of users of class l with trip
purpose o traveling from the origin node to the
destination node.
iI Model Assumptions and Structure
1) Travel cost functions
In single class models are often separable and symmetric,
allowing for convex optimization formulation. In multi-
class models travel costs of one class are affected by
decisions of other classes; hence the cost structure is not
separable, and in general it is not symmetric and does not
allow a convex optimization formulation. We assume the
a) For each link a, the link cost function, A
lonmtlo lolo
CtMn ijR ,
, will depend, in general, upon the flow over all
links, the vector f, in the multimodal network (N, A) for
all user class , trip propose, transport mode
nest , transport mode , and departure
time period , that is
n lo
onmt lonmt
ni 
lon mt
 
lonmt lonmt
1 if li
0 ot
lon mt
 
() ,,
ll olo
, ,
l ol o
 
We will also assume that the perceived cost of travel
on any multimodal route (path), is the sum of
travel costs on the links that comprise that path, that is:
(), ,,
lonmtlonmtl o
aij m
lo lo
mMijRlL oO
 
nk belongs to path
,, ,
lol o
m n
tK mM
 
2) The Jacobian of the link cost functions
()() : ,,
l ol o
 
Cf f
is asymmetric
The function form specification of the link cost func-
tion in (1) depends on the application of the model and
how well this link cost function represents the transport
system supply in the urban area of the study. For exam-
ple, if we consider only three nests of transport modes
named and define them as follows:
12 3
,, and nn n
as pure private transportation modes (e.g., car,
taxi, etc.)
as pure public transportation modes (e.g., bus,
subway, metro, etc.)
nm as combined transportation modes (e.g.,
car/metro, bus/metro, etc.) and used the specified link
cost functions for each of the modes ,
m, and
that used in ESTRAUS [37] in its application to Chilean
city Santiago, MSTEM model will have all the advan-
tages of ESTRAUS from the transport system supply
side representation, especially the capacity constraints
for vehicles of all public transport modes, in addition to,
the advantages of MSTEM model over the ESTRAUS
from the transport system demand side.
That is, the link’s a average operating cost (op-
erating time or generalized a cost), for users of class l,
with trip purpose o, of private transportation mode
(e.g., car, taxi, etc.) depart from his origin at time t. This
is a function of the summation of vehicle flows over all
private transportation modes, user classes and trip pur-
poses (), as well as the fixed flow of public trans-
portation vehicles (
) on linkat time period t, all
measured in equivalent vehicles (e.g., p.c.u.):
aa a
CC fF
 
Although the Jacobian of the cost functions vector is
not diagonal, it does turn out to be symmetrical, given
the functional form supposed for cost functions
(every vehicle, from whichever user class, trip purpose
and private transportation mode, produces the same im-
pact on congestion). Nevertheless, this “symmetry” of
the cost functions for private modes, which is a simplifi-
cation, could be relaxed without changing the problem
formulation and its solution algorithm. If more general
cost functions are used, considering, for instance, that
different classes of users of private modes produce dif-
ferent impacts on road congestion, the Jacobian of these
cost functions will be asymmetric just like the one asso-
ciated to the public transport cost functions. Then the
combined problem is asymmetric, independent of the
particular characteristics of the cost functions for private
modes. For every pure public transportation mode
the pure service networks can be defined as
where m
N is the set of nodes (m
for ground services that use the road network, such as
buses, and m
where NN
for independ-
ent public transportation services, (e.g., metro)) and m
is the set of transit links (route sections) that belong to
mode m.
Copyright © 2010 SciRes. ENG
The generalized time (cost) functions of the public
transportation links, considering the vehicle capacity
constraints as in De Cea and Fernández [40], (sum of
travel time, waiting time, transfer time, fare, etc.) depend
on the vehicle flow over the road network as well as the
passenger flow in the existing services, as follows:
lomtlomtlomt tlomt
lomt lomt
mtlomtmt ss
sWAIT mt mt
Where lomt
C: Average or generalized cost on link
for users of class l, with trip purpose o, of public
transportation mode
m (e.g. bus, subway, etc.) at time
period t.
P: Fare multiplier for users of class l, with trip
purpose o, of public transportation mode m at time
period t.
TAR : Fare related to public transportation link s of
mode m at time period t.
P: Waiting time multiplier for users of class l,
with trip purpose o, of public transportation mode m at
time period t.
, mt
, tm
: Calibration parameters of waiting time
function, for public transportation mode m at time pe-
riod t.
d: Vehicle frequency of public transportation mode
m over the pubic transportation link s at time period t.
CAP : Capacity of public transportation link
mode mat time period t.
V: Passenger flow of class l, with trip purpose o,
belonging to public transportation mode m at time pe-
riod t, and which use public transportation link s.
: passenger flow that competes with lomt
V for the
capacity of transit lines belonging to Bs (flow with the
same trip purpose, user class, mode, and time period
that belong to other public transportation links that com-
pete or reduce the Bs line’s capacity, plus flow from oth-
er purposes, classes, modes, and time periods that also
compete for the Bs lines capacity).
It is easily seen, in this case, that the Jacobian of the
cost function vector is non-diagonal and asymmetric. In
addition, the model considers the existence of combined
modes, for example car/metro (private transportation/
public transportation) or bus/metro (public transporta-
tion/public transportation). In each case, the union of the
pure mode networks that compose them forms the com-
bined mode network. The combined modes () are
considered to be formed by two public transportation
modes. Nevertheless, it is important to stress that this
does not limit the model’s general use, since there is no
problem in representing combined modes such as
car-public transportation (as in the application of ES-
TRAUS for the city of Santiago considers combined
modes like car driver-metro and car passenger-metro).
b) Trip Generation (TG)
Following the same line of thought of Safwat and
Magnanti [41], the accessibility of origin as
perceived from user of class l with trip purpose o travel-
ing from that origin can be defined as follows:
lololo lo
iIlL oO
 j
 
where = the expectation operator.
max 0,lnexp
lo lo lonmtlo
iiij j
jD nmM tK
lololo lo
S(θuA )
iI lLoO
 
 
The number of trips generated from origin for us-
ers of class l with trip purpose o, can be expressed
lolo lololo
iI lLoO
 
 
Similar tolo
, is assumed to be a fixed constant
during the time period required to achieve short-run
equilibrium, and depends solely on the system’s
performance as measured by the accessibility variable
c) Trip Distribution, Nest/Mode Split, and Departure
Time Logit Models (TD/MS/DT)
Following the same line of thought of Safwat and
Magnanti [41], Oppenheim [42] and Ran and Boyce [43]
our distribution, nest/mode, and departure time Logit
models can be given by:
lo lonmtlo
iij j
lo lo
ij ilo lonmtlo
iij j
jD nmM tK
lolo lo
lololo lo
θuA )
TG θuA
ijRlLo O
 
 
lo lonmtlo
iij j
mM tK
lon lo
ij ijlo lonmtlo
iij j
lo lo
lo lo
lolo lo
θuA )
TT θuA
 
 
Copyright © 2010 SciRes. ENG
M. K. HASAN.281
lo lonmtlo
iij j
lonm lon
ij ijlo lonmtlo
iij j
mM tK
lo lolo
lo lo
(θuA )
TT (θuA )
mMnijR lLoO
 
exp() ,
lo lonmtlo
iij j
lonmt lonm
ij ijlo lonmtlo
iij j
lolo lo
TT (θuA )
tK mMn
 
 
d) Trip Assignment (TA)
Based on the previous choices assumption, the given
user will choose his or her route according to Wardrop’s
user equilibrium principle. That is, for all users of class l
with trip purpose o traveling from the origin node
to the destination node and whose already
chose the mode of transport from the nest of
modes and start their trip at the time ,
the perceived generalized costs on all used multimodal
paths between the given origin-destination pair are equal
and not grater than those on unused paths. This gives the
following equilibrium conditions:
if 0,
if 0
lonmt lonmt
ij p
plonmt lonmt
ij p
lonmt lolo
ijm n
lo lo
pPtK mM
 
,, ,
lonmtlonmt lonmt
lL oO ijRp P
lo lo
lo lo
lo lonmt
 
 
  Variational Inequality Formulation for
Because of the asymmetry of the link cost functions,
MSTEM cannot be cast as an equivalent optimization
program as STEM. Instead, it can be formulated as the
following variational inequality (VI).
** **
feasible ,
CfffUTT T0
fT (12)
f: vector of flow on links of the multimodal network
f: vector of equilibrium flow on links of the multimo-
dal network
T: vector of trips between origin-destination pairs of the
multimodal network
(:, ,
ilo lo
iI lLoOTT
T: vector of equilibrium trips between origin - destina-
tion pairs of the multimodal network
()Cf : column-vector of network link's cost functions
(with non-diagonal and asymmetric Jacobian)
()UT: column-vector of inverse demand functions
(with non-diagonal and symmetric Jacobian),
u, ,
iI lLoO)
The VI problem in (12) is Equivalent to MSTEM (see,
for example, Smith [44,45] and Dafermos [46] for a for-
mal proof of equivalency between VI and traffic equilib-
rium) and can be solved by the relaxation (diagonaliza-
tion) algorithm (see, for example, Dafermos [47], Florian
and Spiess [48], Mahmassani and Mouskos [49], and
Sheffi [50])
At each iteration of the diagonalization algorithm, the
cost functions of (1) result in diagonalized cost
function , which depend only on their own
, and the following VI should be solved:
** **
ˆ()()( )()
feasible ,
CfffUTT T0
This VI can be formulated as the following Equivalent
Optimization Program (ECP):
lL oO iIjD nmMtK
lolololo lo
Min Z
 
ˆ()[( )
lonmt lo
aAlLoO iI i
Cxdx S
lo lolo lololo lolo
iii i
 
 
lonmt lonmt
ij ij
lL oO iIjD nmMtK
lolololo lo
 )
iI lLoO 
lo lo
iIjD lLoO
lo lonmtlonmt
jij ij
AT T
lolo lolo
iji i
lo lon
ij ij
lon lonm
ij ij
,, ,,
lo lolo
iIjD nlLoO
ij iji
lo lo
mMnlL oO
Copyright © 2010 SciRes. ENG
lonmtlonmt lo
ij p
lo lolo
jD tKmM
 
 
iI lLoO 
lo lo
iIjD lLo
,, ,
lo lolo
iIjD nl 
lo lolo
iIjD mM
 
 
, ,
lo lo
lo lo
 
 
, ,
lo lo
lo lo
mM n
 
 
,, ,
lonmtlonmt lonmt
lL oO ij Rp P
lo lo
lo lo
lo lonmt
aAtK mM
 
 
 
T ,LoO 
Existence, convexity and uniqueness of ECP problem
as well as equivalence between MSTEM and ECP can be
followed as those of Safwat and Magnanti [41].
The Logit Distribution of Trips (LDT) algorithm that
developed by Safwat and Brademeyer [51] can be modi-
fied as Multiclass Logit Distribution of Trips (MLDT)
algorithm to solve the above ECP. For more details about
the MSTEM methodology see Hasan and Dashti [36].
3.3. Data Collection Requirements
To apply the MSTEM to any urban transportation net-
work we need the following data collection:
1) Household Survey
Trips mad by all household members by all modes of
transport both within the study area and leaving/arriving
to the area during the survey period
Personal and household characteristics and identi-
- Head of household, wife, son,…, etc.
- Sex, age, possession of a driving license, education
level, and occupation
Socio-economic information: income, car owner-
ship, family size
Trip data: origin, destination, purpose, start and
ending times, mode used, amount of money paid for the
trip, and so on
2) Traffic Counts
An actual observed traffic counts in specific locations
in the city road and highways should be collected for at
different time of the day (specifically in the peak hour),
different day of the week, and for different kind of vehi-
cles types.
3) Roadside Interviews
These provide useful information about trips not reg-
istered in the household survey (i.e. external-external in
cordon survey). It involves asking a sample of drivers
and passengers of vehicles (e.g. cars, public transport,
goods vehicles) crossing a roadside station, a limited set
of questions; these include at least origin, destination and
trip purpose.
4) Network inventory
a) Road network
The following link attributes for each link:
1- From node and To node
2- Length
3- Its travel speeds-either free-flow speeds or an ob-
served value for a given flow level
4-The capacity of the link
5- Type of road (e.g., expressway, trunk road, local
6- Road width, or number of lanes, or both
7- An indication of the presence or otherwise of bus
lanes, or prohibitions of use by certain vehicles (e.g.,
8- Banned turns, or turns to be undertaken only when
suitable gaps in the opposing traffic become available
9- Type of junction and junction details including sig-
nal timings
Storage capacity for queues and their presence at
the start of a signal
b) Transit network
1. Lines
2. Fares’
3. Frequency
4. Travel time measurements
5) Land use inventory
a) Residential zones (housing density)
b) Commercial and industrial zones (by type of estab-
c) Parking spaces
6) Cordon Surveys
These provide useful information about external-
external and external-internal trips. Their objective is to
determine the number of trips that enter, leave and/or
cross the cordoned area, thus helping to complete the
information coming from the household O-D survey.
7) Screen-Line Surveys
Screen lines divide the area into large natural zones
Copyright © 2010 SciRes. ENG
M. K. HASAN.283
(e.g. at both sides a river or motorway), with few cross-
ing points between them. The procedure is analogous to
that of cordon survey and the data also serve to fill gaps
in and validate the information coming from the house-
hold and cordon surveys.
8) Socio-economic data
To compute lo
, the value of the th
mic variable that influences the number of trips gener-
ated from origin i for users of class l with trip purpose o,
the Socio economic data for each origin i could be popu-
lation, income, car ownership, ….etc.
To compute lo
, the value of the socio-econ-
omic variable that influences trip attraction at destination
j for users of class l with trip purpose o, The socio-
economic data for destination node j could be the ground
floor area of the land use type of destination j , employ-
ment size, …..etc.
3.4 Analysis and Evaluation of Effective
Solutions for Peak-period Traffic Congestion
Peak-period traffic congestion occurs when travel de-
mand exceeds the existing road system capacity, Sandra
Rosenbloom [52]. There are two basically alter native-
I Change demand (Users Characteristics and Beha-
vior in Figure 2) to meet system capability
II Change system capacity (Owners, Operators and
Regulators Behavior in Figure 2) to meet demand.
The demand for road system capacity may be changed
a) Reducing the number of vehicles used to meet the
existing travel demand by increasing vehicle occupancy.
b) Reorienting travel to off-peak periods.
c) Reorienting travel to less congested alternative
d) Reducing the total demand for travel itself.
System capacity itself can be changed by:
a) Constructing additional roadway, either adding
lanes to existing routes or providing new routes or new
transportation modes.
b) Increasing the capacity of the existing road infra-
structure by engineering techniques that improve traffic
Sandra Rosenbloom [52] shows that 22 techniques
(see Table 1) which appeared to have the potential to
reduce or redistribute demand were fell into four major
categories of approaches:
a) social approaches which seek to alleviate conges-
tion utilizing techniques that change social behavior or
personal interactions
b) socio-economic approaches which utilize tech-
niques that induce favorable travel changes by manipu-
lating broadly defined economic penalties and incentives
c) socio-technical approaches which utilize technol-
ogy resources to modify social and economic behavior in
ways that ultimately reduce congestion
d) technical approaches which use technical devices to
modify directly dysfunctional travel behavior at the im-
mediate congestion site or source.
These Twenty two techniques were grouped under
these four major approaches cover a wide range of social,
behavioral and economic incentives, it was extremely
difficult to make comparisons among them. This diffi-
culty was heightened because these techniques affected
peak-period traffic congestion directly and indirectly in
different ways. Sandra Rosenbloom [46] illustrated the
differing impacts of these techniques in practice where
most of the non-engineering techniques considered to be
applicable to Peak-Period traffic congestion were not
designed with the reduction of peak period traffic con-
gestion as their primary goal; the reduction of traffic
congestion was usually considered to be a secondary or
indirect impact of their implementation. For such tech-
niques to significantly affect traffic congestion, they
must have first successfully met some other goaltheir
primary goal. However, it was often difficult to get pro-
ject goals clearly articulated, and the relationship be-
tween those goals and traffic congestion identified.
Based on this analysis (see Sandra Rosenbloom [46]),
the study team evaluated 17 of these techniques as both
effective and feasible in a U.S. institutional context.
However, none of these 17 offered more than marginal
reductions in peak-period traffic congestion when ap-
plied individually. Some techniques affected so small a
percent-age of travelers that reductions in congestion
would not be discernible. Other techniques promised
significant congestion reductions in theory but did not
realize that promise in practice. It was concluded that
many techniques could be implemented together with the
potential for far greater combined effectiveness. An
analysis was performed to determine how best to "pack-
age" or jointly implement promising techniques to opti-
mize their combined effectiveness. It was found that all
promising techniques could not be applied together be-
cause of conflicts in their impact. This analysis suggested
eight sample "packages" or combinations of mutually
supportive techniques. These eight packages were sub-
jected to evaluations similar to those performed for indi-
vidual techniques; while the packages are merely exam-
ples of potential combinations, the evaluation methodol-
ogy employed should be of continuing use to local
transportation planners.
The development of the packaging concept is consis-
tent with the development of the Transportation System
Management (TSM) concept in the U.S. The conclusions
of the study should also have merit for metropolitan ar-
eas considering management schemes. They are:
- Promising congestion reduction techniques or pack-
ages must be applied to the appropriate congestion situa-
Copyright © 2010 SciRes. ENG
- Promising techniques have not been successful alone
in significantly affecting peak-period traffic congestion
- All promising techniques cannot be implemented to-
gether because some seriously conflict with one, and -
Promising congestion reduction techniques may have
serious social, economic and physical side-effects which
must be recognized prior to implementation
The above study summary shows the guidelines based
on empirical results that can be helpful for city planner,
traffic engineers or transportation authorities' decision
makers. But how we help these people to perform these
analysis and guidelines in some systematic or even intel-
ligent way?
4. The Architecture of the Intelligent
Decision Support System for Traffic
Congestion Management System
The architecture and the information flow shown in Fig-
ure 3 represent a high level blueprint for the implemen-
tation of the framework for an Intelligent Decision Sup-
port system Traffic Congestion Management System.
The framework is derived and guided by the methodo-
logical framework for urban transportation system analy-
sis presented in Section 3 and depicted in Figure 2 and
the adoption of the main components of the Decision
Support System (DSS) is guided by standard DSS text-
book such as Turban et al. [53]. A fourth component, the
scenario management was added to package the func-
tionally required by scenarios creation, storage, retrieval,
analysis, evaluation and reporting. An Intelligent Agent
for supporting scenario creation is also included in the
frame-work. Figure 3 shows the main components of the
DSS, their interactions (data and control flows) with the
Transportation Object Repository, with each other and
with the User Interface Management Subsystem (UIMS)
directly or indirectly. In the following section
we briefly describe each of these components.
4.1. Urban Transportation Object Repository
The Urban Transportation Object Repository (UTOR) is
an object oriented repository storing various transporta-
tion objects such as nodes, links, and zones for multi-
modal urban networks; equilibrium models; impact
models, scenarios and user interfaces that are managed
by the four subsystems discussed bellow. The four
UTOR object stores are distinct but are integrated. These
1) Urban Transportation GIS-Data Store: This object
store contains two distinct components:
Table 1. Four approaches and 22 techniques reduction of
peak period traffic congestion.
Social Approaches
- Staggered Work Hours
- Shortened Work Weeks
Socio-Economic Approaches
Pricing and Regulatory Mechanisms
- Road Pricing
- Parking Controls
- Restricting Access
-Traffic Cells
- Auto-Free Zones with Facilities for Pedestrians
Land Use Planning
-New Towns
-Planned Communities
-Planned Neighborhoods
-Zoning and Building Codes
-Incentives to Off-Peak Travel and Usage of Facilities
-Incentives to Mass Transit Usage
-Carpooling and Other Forms of Ride-Sharing
Socio-Technical Approaches
-Communications in Lieu of Travel
Technical Approaches
Traffic Engineering Techniques
-Freeway Surveillance and Control
-Traffic Simulation Models
-Maximum Use of Existing Facilities
Transit Operations
-Extended Area Services
-Priority Systems: Expressways and Freeways
-Priority Systems: Arterials
Circulation Systems
Vehicle Design Factors
a) GIS Object Base: Contains various GIS Objects.
b) Transportation Data Warehouse (TDW). TDW is a
multidimensional, object-oriented, nonvolatile integrated
database containing various current and historical data
about the transportation Objects (Inmon [54]). The TDW
and GIS Objects are populated by the Extract Transform
and Loading (ETL) component of Data Management
subsystem (DMS) mentioned bellow.
2) Urban Transportation Model Base: contains various
transportation equilibrium and impact models.
3) Scenarios Base: contains various scenarios objects
that are created over times.
4) User Interface Base: contains various user interface
(UI) objects created over time.
4.2. Data Management Subsystem (DMS)
The data management subsystem (DMS) is responsible
for the data administration such as creation, storage, re-
trieval of node object, links object, and zone object for
different modal network. DMS manages the Urban
Transportation GIS-Data Store which contains two dis-
tinct but integrated data bases:
a) a GIS database contain the spatial data;
b) a transportation data warehouse mentioned above.
Copyright © 2010 SciRes. ENG
M. K. HASAN.285
4.3. Extract, Transform and Load (ETL)
ETL component of the DMS extracts data from multiple
sources, cleanse them and transform the data from its
original to a form that could be place in TDW guided by
the metadata, and then load the data into the TDW. The
purpose of ETL is to populate TDW with integrated and
cleansed data required by the transportation objects ga-
thered from three main sources, the socio-economic, the
demand data and the supply data.
4.4. Model Management Subsystem (MMS)
MMS is responsible for the creation, storage, retrieval of
the transportation object models which are stored in the
Model Object Base (MOB) for utilization or reuse.
Two types of models are managed by MMS. These are:
a) The Transportation Network Equilibrium Models
b) Impacts Models: such as impact on Users, impact
on operators, impact on owners, impact on society, and
impact on government discussed earlier in Section 3.
4.5. Scenario Management Subsystem (SMS)
Much like MMS, SMS is responsible for the creation,
storage, retrieval, analysis, evaluation of scenarios. An
important part of SMS is the impact evaluation compo-
nent that assesses the impact models and presents the
assessment results to the SMS. Objects are stored in
Scenario Object Base (SOB) for future utilization or re-
use by SMS and Intelligent Scenario Creation Assistance
Agent (ISCAA) described bellow. SMS retrieves previ-
ously created scenarios and pass them to the User Inter-
face Management Subsystem (UIMS) as initial scenarios
on which further what-if analysis could be performed.
4.6. Intelligent Scenario Creation Assistance
Agent (ISCAA)
The complexity of creating the right scenario or retriev-
ing the right scenario from the previous created ones
stored in the scenario base is a complex process requires
human expertise which is scarce. An intelligent compo-
nent within the DSS framework that would look at the
historical scenario objects and assist and guide the deci-
sion maker in choosing the best alternatives from this
pool of historical scenario object to be included in the
initial scenario setup is extremely valuable. A solution
for this problem is to create an Intelligent Scenario Crea-
tion. Assistance Agent (ISCAA) that would encode and
encapsulates the expertise for scenario creation and
would provide the necessary assistance for creating the
right scenario. ISCAA would be a hybrid intelligent
agent containing multiple computational intelligent tools
(such as ANN, Rough Set, Fuzzy Logic, etc.) as well as a
set of scenario creation rules.
4.7. User Interface Management Subsystem
The UIMS packages and manages the functionalities
require for creating a data-rich intensive (maps, graphs,
text, and structured data) with various visualization ca-
pabilities user interface. Since The DSS is to be used by
users with various roles (Transportation Planners, Trans-
portation Engineers, Transportation Decision Makers or
Traffic Administrators), the complexity involves in dy-
namically creating the right graphical user interface (GUI)
for the right role lies within the functionality of UIMS.
For example, the transportation planner is responsible for
creating models and capturing the right data for those
models, as such he/she would directly interact with
Model Management and Data Management and as such
the GUI for this role would configure that would allow
for that only. On the other hand, the transportation deci-
sion maker role deals with scenarios and as such the
UIMS would create the proper GUI allowing various
scenario related activities such as scenario creation, re-
trieval, storage and execution and presenting the result in
a dashboard view allowing for a comprehensive, at-a-
glance, GIS-Based graphical view of the solution gener-
ated. UIMS also provides various analysis tools such as
what-if analysis, sensitivity analysis, reporting the result
of impact evaluation and providing various Ad hoc que-
ries and reports. The Graphical Interface Objects, that are
created, are stored as UI Objects in the UTOR and man-
aged by UIMS.
All components of CMS can be adapted through the
Users Characteristics and Behavior (Activity System
Options) and Owners; Operators; and Regulators Behav-
ior (Transportation System Options) in Figure 2, in ad-
dition, to any roadway capacity expansion through the
Transportation System Options. In other word, our frame-
work in Figure 2 can be adapted to any of the single 22
techniques in table one or any combinations (packages)
of these techniques, but the evaluating and recommend-
ing alternative strategies for CMS will be done through
the results of the total evaluation of Impact Models (the
positive and negative parts) on Users, Owners, Operators,
Regulators, Government, and Society at large for each
alternative or packages of alternations. All the alterna-
tive can be reflected through the MSTEM model demand
and performance (link cost functions) parameters and
socio-economic and land use variables.
5. Summary, Conclusion and Future
5.1. Summary
Traffic congestion is a very complicated problem that
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Copyright © 2010 SciRes. ENG
affects the social, economic and development aspects of
many countries around the world. The relationship be-
tween the traffic congestion problem and socioeconomic
characteristics of trip makers and the rapid increase of
urbanization of the land use make the problem becomes
much more complicated and calls of urgent solution.
Many techniques for solving this problem were sug-
gested starting from empirical studies for a single tech-
nique or packages of techniques , to advanced Transpor-
tation System Management (TSM) and finally to Con-
gestion System Management (CSM). Most of these tech-
niques tried to maximize the utilization of the existing
transport system (Roads, Highways, and Transit systems)
by certain management techniques prior to any expan-
sion or addition to the transport system.
US Federal transportation legislation requires Metro-
politan Planning Organizations (MPO) to develop and
implement a CMS as part of the metropolitan transporta-
tion planning process [22]. Most of MPOs use traditional
transportation planning modeling approach and a com-
mercial specialized software as a part of this process to
evaluate traffic relive and the length and cost for deferent
alternative solutions prior to submitting their recom-
mendations. This process requires highly educated,
trained and expert personnel in transportation planning
and engineering field. It would also require very user
friendly transportation planning software that includes a
state of the art transportation network equilibrium mod-
els. Models that are significantly more advanced than the
traditional four steps sequential. Models that combine
single class Trip Generation- Trip Distribution- Modal
Split-Trip Assignment (TG-TD-MS-TA) [41], or a mul-
ticlass combined model [36] that combine TG-TD- MS-
TA in addition to Departure Time for different class of
traveler depend on income, car ownership, trip pur-
pose, … etc.
As Boyce [55] says, “combined or integrated models
of origin-destination, mode, route and time period choice
can be implemented and solved with practitioner soft-
ware systems such as CUBE, EMME/2, PTV Vision,
QRS II, SATURN or TransCAD, if the travel forecaster
has a detailed understanding of the models and the solu-
tion algorithm. Ongoing discussions with practitioners
reveal that few practitioners have this requisite expertise.
The observed inability of many practitioners to solve the
sequential procedure with feedback in a convergent
manner is one indication of this dilemma. An alternative
approach is to design a software system which directly
solves such an integrated model, including providing a
number of options to the user. This approach was taken
by the Chilean software vendor, MCT, in the develop-
ment of its product ESTRAUS. The distinction between
these two software development philosophies may be
more subtle than is generally appreciated. The former
approach offers the forecaster a “tool kit” with which to
“build” a model. Then the practitioner must acquire the
expertise to use it, an uncertain and arduous process with
many pitfalls. The latter provides a “canned” model and
an algorithm for solving it. When presented with a de-
scription of ESTRAUS, a typical user’s comment is,
“This is interesting, but I don’t know how we would use
it to solve our model.” This comment may offer a clue to
the dilemma of vastly upgrading or revolutionizing prac-
tice, which presumably is one of the aims of academics
as well as software vendors. Would a standard set of
models with numerous user options provide a more ef-
fective pathway to improving travel forecasting practice
than attempting to upgrade practice model by model?
Certainly, this option is worth considering, so long as
several of the vendors can effectively participate in the
software market”.
5.2. Conclusions
The main conclusions of this research paper can be
summarized as follows:
1) In this research paper, it was possible to integrate
all of the above requirements in a unified framework of
analyses that capture the concept of the interaction of the
transport system with the activity system in one Trans-
portation System Analysis (TSA) approached. Develop-
ing this TSA framework as a TCMS within an Intelligent
Decision Support System (IDSS), that can be a useful
decision support tool for transportation planners and
transportation decision makers for the analysis and
evaluation of strategic transportation plans include
transportation projects and policies and utilizing the stat
of the art of the Multiclass Transportation Equilibrium
Model MSTEM [36] within an easy to use environment
that would eliminate or reduce the necessity for highly
educated and trained transportation specialists and deci-
sion makers, is the cornerstone of this paper.
2) The paper presents many transportation concepts
and integrate all of them in such a comprehensive
framework that is easy to follow and undetectable for
practitioners transportation planners, transportation En-
gineers, or transportation decision makers.
3) The Intelligent Scenario Creation Assistance Agent
(ISCAA) and the Scenario Management Subsystem
(SMS) components of the IDSS architecture will be the
most important components that will reduce the need of
the expertise and high education level of the transporta-
tion planners and transportation engineers or any trans-
portation decision makers. This IDSS will reduce the gap
between the practitioners and software vendors and in-
crease the usability of the most sophisticated and more
behaviorally relevant Multiclass Transportation Equilib-
rium Model like MSTEM.
5.3. Future Research
The author started a development of data collection pro-
M. K. HASAN. 287
Figure 3. The architecture and information flow of the intelligent decision support system for urban transportation systems
nalysis as TCMS. a
gram for Saint Louis Region area during his sabbatical
year at University of Missouri at Saint Louis. These data
will be used in the calibration and validation process for
STEM, MSTEM, and ESTRAUS models for future
comparisons among these models. The results of these
comparisons will be reported in upcoming papers
The development of a prototype, derived from the
framework described in this paper is also under way.
The result of the experimentation with the prototype, in
terms of its usability, degree of ease and its effectiveness
in providing optimum solutions/scenarios to transporta-
tion and decision makers will be reported in upcoming
6. Acknowledgements
The author wish to thank the Research Administration
(RA), Kuwait University for granting the financial assis-
tance and sponsoring this research. This research is sup-
ported by Kuwait University, Research Grant No.
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