Application of DSS to Traffic Management

ODUBANJO EZEKIEL

Table of Contents

Table of Figures ............................................................................................................................................. 1

Table of Tables .............................................................................................................................................. 1

Introduction .................................................................................................................................................. 2

Framework and Infrastructure ...................................................................................................................... 3

MODELLING AND APPLICATION OF DSS TO TRAFFIC MANAGEMENT .......................................................... 8

MSTEM By (Hasan, 2010) .......................................................................................................................... 8

Fuzzy Logic Application by (Almejalli, Dahal, & Hossain, 2006) .............................................................. 12

MultiAgent Modelling by (Ossowski, et al., 2005) .................................................................................. 15

Conclusion ................................................................................................................................................... 18

References .................................................................................................................................................. 19

Table of Figures

Figure 1: Basic Variables and relationships in transportation system analysis ............................................ 3

Figure 2: Hassan M.K Framework ................................................................................................................. 4

Figure 3: Abstract dialogue between DSS and DM. ...................................................................................... 5

Figure 4: Social role model for DSS ............................................................................................................... 6

Figure 5: Influencing Factors and Typical Decisions - Almejalli et al. ........................................................... 8

Figure 6: The structure of the traffic fuzzy inference system ..................................................................... 12

Figure 7: The architecture and information flow of the intelligent decision support system for urban

transportation systems analysis as TCMS by Hasan M.K. ........................................................................... 14

Figure 8: Bilbao before DSS application ...................................................................................................... 16

Figure 9: Bilbao after DSS application ......................................................................................................... 17

Table of Tables

Table 1: Four approaches and 22 techniques reduction of peak period traffic congestion. ...................... 11

Table 2: SELECTIVE RULES FROM THE RIYADH TRAFFIC RULE-BASED STRUCTURE .................................... 13

Introduction

One of the most inevitable problems we encounter in our major cities all over the world is traffic

management. There are always many reasons leading to traffic congestion ranging for accidents,

to insufficient road space, traffic violations, recklessness on the part of the drivers, improper

traffic management systems, etc. Few cities all over the world have been able to manage their

traffic congestion issues to a bearable minimum and one of the tools that have been used in

achieving this milestone is the DSS. Decision Support Systems (DSS) provides assistance to

humans involved in complex decision making process. Modern DSS help decision makers to

explore the implications of their judgments so as to make decisions based on their understanding.

Due to the inherent distribution of domains where humans have to make operational decisions

regarding the management of complex industrial or environmental processes, (Cuena J. &

Ossowski, 1999) asserted that the construction of DSS have become popular.

Many researchers agree that Transportation Management is a problem in urban areas all over

the world and DSS is a tool that can be used to proffer solution to this great problem facing the

uni9verse. Various models of DSS and solutions as suggested by three of the studies that had

been conducted in this field. The three papers used in this study are (Hasan, 2010), (Ossowski, et

al., 2005) and (Almejalli, Dahal, & Hossain, 2006).

Framework and Infrastructure

Figure 1: Basic Variables and relationships in transportation system analysis

The framework used by (Hasan, 2010) has its components such as the socio-economic

environmental description, user characteristics and behaviors, owner and operator’s behavior,

transport demand, transport equilibrium, systems, performance, traffic flow impacts, evaluation

and choice and finally implementation of the model.

Socio-Economic Environmental description:

The factors considered here include the population distribution, income, car ownership, and land

use of the study area. Also, 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.

User Characteristics and behavior:

This part represents the transportation demand side where there are, for example, the following

residence locations, economic& social activities locations, frequency, destination, purpose, travel

mode and time of trip, work locations and trip route.

Owners, Operators and Regulators Behavior

Represented in this part are the transportation system performance which consists of network

configurations, investment and maintenance policies, vehicle fleets characteristics, routing,

scheduling and pricing policies, laws, regulations and controls and institutions.

Transportation System Performance

This is as a result of functions of transportation supply and transportation demand.

Figure 2: Hassan M.K Framework

In analyzing the Framework and infrastructure, (Ossowski, et al., 2005) described a design

method for knowledge-based DSS based on a multi-agent architecture. This method builds upon

the RICA (Role, Interaction, Communicative Act) theory, which like the mainstream agent-

oriented software engineering (e.g. Wooldridge et al., 2000), conceives multi-agent system

design in terms of organizational concepts (e.g. Ferber et al., 2000, or Zambonelli et al., 2000). It

was established that there is always a communication between decision makers (DMs) and the

DSS They first came up with an organizational model where they identified four types of

communication interaction for decision support namely Information exchange (a conversation

about the truth of some proposition), Action performing (a conversation respecting the

realization of some action), Explanation (a conversation about the reasons for the truth of some

statement) and Advice (a conversation about the (subjective) benefits of some action). This led

to the formation of the abstract dialogue shown in fig 3 below.

Figure 3: Abstract dialogue between DSS and DM.

In addition to the organizational model, (Ossowski, et al., 2005) came up with the agent model

with two social roles one agent—several roles and one role—several agents through which

different abstract agent types for DSS were developed. The model is described in Figure 4 below

with major agents like the management agents, user interface agents and peripheral agents.

Figure 4: Social role model for DSS

After some expert information collection and analysis using the traffic in Riyadh region in Saudi

Arabia, (Almejalli, Dahal, & Hossain, 2006) came up with influencing factors, typical decisions and

fuzzy logic in decision making that forms the framework and need for traffic management system.

Identified as the first and most important factor is the traffic violation. A large record of violations

on the traffic scene leaves the impression that the measures put in place by the traffic regulation

agencies are either not functional or insufficient. This measures include traffic policemen,

cameras, etc. It could even be considered that the fines associated with traffic violations are

either not enforced or are very much affordable by the citizens so they find it fun breaking the

rules knowing that they will only be charged a meagre.

The number of traffic accidents is another worrying factor as it is one inevitable fact that the

transport system always look to completely extinct but it still occurs regardless the number of

cautions and measures placed. Two other factors considered are the number of vehicles per

traffic policeman on duty and the number of vehicles per traffic camera. The number of vehicles

per traffic policeman is calculated by dividing the number of vehicles by the number of traffic

policemen on duty, while dividing the number of vehicles by the number of traffic cameras yields

the number of vehicles per traffic camera. These two parameters are determined as influencing

factors because they are considered as a measure of controlling and monitoring traffic.

Thus, to keep the traffic controlled, the number of vehicles per policeman and the number of

vehicles per traffic camera should not be high. The values of these two factors in a particular

period are based on how many vehicles are used at that period, and also on how many traffic

policemen and traffic cameras are available at that period.

Considered the least important among the factors is the rate of traffic jam as it does not directly

affect the traffic decision but it does affect traffic accidents.

Based on the aforementioned factors, the decisions that are to be made are as follows;

1. Launch traffic awareness campaign

2. Increase punishment or fine

3. Increase the number of traffic policemen

4. Increase the number of traffic cameras

The mapping of the decisions to the factors can be seen tin figure 4.

Figure 5: Influencing Factors and Typical Decisions - Almejalli et al.

MODELLING AND APPLICATION OF DSS TO TRAFFIC MANAGEMENT MSTEM By (Hasan,

2010)

The model used by (Hasan, 2010) is known as the Multi-class Simultaneous Transportation

Equilibrium Model (MSTEM) which combines 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 classification of (Boyce & Bar-Gera, 2004). The MSTEM includes

all the features of ESTRAUS in addition to the other features mentioned above and more flexible

structure for demand models where the trip generation can depend upon the system’s

performance through an accessibility 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 behaviorally

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).

ESTRAUS is a computer package for solving supply demand equilibrium problems on multimodal

urban transportation networks with multiple user classes. This was a software designed in Chile

in the 1989. It has been used for analyzing and strategic projects for development plans for the

city of Santiago in Chile, whose population is near, 5.5 million inhabitants (De Cea, Fernandez,

Dekock, & Soto, 2002). The MSTEM is formulated as a Variational Inequality problem and a

diagonalization (relaxation) algorithm is proposed to solve it.

Just as in the ESTRAUS, the different modal networks considered in the MSTEM are car, bus,

metro, shared taxi, bus/metro, shared taxi/metro and car/metro networks.

The notations used for the MSTEM model include;

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-based-shopping, etc.)

O = Set of all trip purpose.

I lo = Set of origin nodes for user class l and trip purpose o

i = An origin node in the set I lo for user class l with trip purpose o

𝐷𝑖𝑙𝑜= 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 𝐷𝑖𝑙𝑜 for user class l 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

i∈Ilo and destinations j∈𝐷𝑖𝑙𝑜

M = Any transportation mode in the urban area

n= Nest of transportation modes that has a specific 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 origin-destination pairs mij

An example of the complex notifications used in the model is

𝑇𝑖𝑗𝑙𝑜𝑛𝑚= the number trips of users of class l with trip purpose o traveling from the origin node i∈Ilo

to the destination node j∈𝐷𝑖𝑙𝑜 and whose already chose the mode of transport m∈𝑀𝑛

𝑙𝑜 from the

nest of modes n∈∧𝑖𝑗𝑙𝑜 and start their trip at the time t∈𝐾𝑚

𝑙𝑜.

The assumptions generated from this model included the travel cost functions, trip generation,

Trip Distribution, Nest/Mode Split, and Departure Time Logit Models (TD/MS/DT) and trip

assignment all based on the notations described above.

In the application of the MSTEM to traffic management, (Hasan, 2010) recommended that

household survey data, traffic counts, road side interviews, network inventory (road network and

transit network), parking spaces, cordon surveys, screen-line surveys and socio economic data

are necessary information that must be gathered to have an efficient application of the model in

solving traffic management problem.

Furthermore, (Hasan, 2010) analyzed changing demand (Users Characteristics and Behavior in

Figure 2: Hassan M.K Framework to meet system capabilities and changing system capacities

(Owners, Operators and Regulators Behavior in Figure 2: Hassan M.K Framework) to meet

demand. Four approaches which included 22 techniques were revealed to be the solution to

reduction in demand. The approaches and techniques are shown in Table 1: Four approaches and

22 techniques reduction of peak period traffic congestion.

Table 1: Four approaches and 22 techniques reduction of peak period traffic congestion.

Fuzzy Logic Application by (Almejalli, Dahal, & Hossain, 2006)

The Fuzzy Logic saw the analysis of some parameters and the factors stated above to form a

framework for the transport management system. This involved some logic simulations involving

the IF X AND Y THEN Z rules and deffusification (Rule Box) to come up with the inference shown

in fig 2. The justification of the output is seen in the table 1.

Figure 6: The structure of the traffic fuzzy inference system

Table 2: SELECTIVE RULES FROM THE RIYADH TRAFFIC RULE-BASED STRUCTURE

Architecture

The architecture analysed by (Hasan, 2010) is represented by a high level blueprint for the

implementation of the framework for an Intelligent Decision Support system Traffic Congestion

Management System. The framework is derived and guided by the methodological framework

for urban transportation system analysis and depicted in Figure 1. A fourth component, the

scenario management was added to package the functionally 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 7 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.

Figure 7: The architecture and information flow of the intelligent decision support system for

urban transportation systems analysis as TCMS by Hasan M.K.

MultiAgent Modelling by (Ossowski, et al., 2005)

The application of DSS to traffic management by (Ossowski, et al., 2005) saw the citation of two

places in Spain. Malaga and Bilbao. This section saw the application of the agents described in

the framework to traffic problems in this major cities.

The problem scenario in Malaga is seen below

“The first application of the multiagent DSS architecture refers to the domain of road traffic management.

In particular, we are concerned with part of the high-capacity road network in the Bilbao area. Regular

information about the traffic situation in this highly used area, registered by loop detectors, is received in

the Mobility Management Centre located at Malmasin, in the vicinity of the city of Bilbao. On the basis of

this data, traffic operators have to take decisions on what the control actions to take to solve or minimize

congestion. These actions include: (1) displaying messages on Variable Message Signal (VMS) panels

installed above the road to warn drivers about traffic problems or recommend alternative routes and/or

(2) contacting local authorities to send the right people to manage the situation. As the traffic control

infrastructure becomes more complex, there is an increasing need to assist operators with their

management task, helping them to configure consistent control plans for the whole road network, and

making the best use of the available signal devices from a global perspective. This is precisely the purpose

of the Bilbao DSS demonstrator described in the following sections”

The Problem Scenario in Bilbao is seen below

“In many big cities, innercity buses are equipped with radio and GPS devices that provide operators in a

Bus Fleet Management (BFM) centre with up-to-date information on bus locations, and can be used to

communicate with the drivers. A typical task of a BFM operator is to detect incidents (buses that are behind

or ahead of schedule, breakdowns,. . .) by comparing a bus_ schedule with its current location data and to

send orders or control actions to bus drivers (increase/reduce speed, switch from timetable regulation to

frequency regulation,. . .) so as to maintain or re-establish an acceptable quality of service.

In the Spanish town of Malaga, control centre operators of the local transport consortium (EMT, Empresa

Malaguena de Transporte) use an Operating Support System that displays information related to the

status of the buses with regard to the scheduled services. There is a line inspector for each line who takes

decisions to adjust services to unforeseen circumstances, and supervisors who are in charge of taking

broader and more complex decisions for several lines. In the following sections, we report on the

architecture of a prototype DSS that extends the functionality of the Operating Support System by

engaging in dialogues with EMT operators concerning the causes of the problems and the best control

actions to take.”

Figure 8: Bilbao before DSS application

Figure 9: Bilbao after DSS application

The Figures Figure 8 and Figure 9 above show the trial zone for the Bilbao demonstrator before

and after the application of DSS. The respective problem detection agents detect the problems

from the traffic data sent by the DAs and inform the subscribed CAs. Congestions in areas 2 and

6 are reported to CA Arena whereas congestion in area 11 is reported to Demetra. After gathering

information on the general situation in neighbouring areas (querying PDA12 and PDA7,

respectively), Atena generates potential solutions for the congestions in problem areas 2 and 6,

while Demetra configures signal plan proposals for the problem detected by PDA11.

Every CA in the DSS knows with which other CAs it is likely to come into conflict. Therefore, once

Atena and Demetra have achieved a consistent signal proposal for their control zones (making

use of local conflict resolution knowledge), they inform each other about these proposals to

identify potential non-local interdependencies. In the first stage of the example scenario, only

congestions in areas 2 and 6 are active, so Atena defines appropriate solutions using panels P1,

P3 and P4, where drivers are warned about the congestion and recommended to take the route

associated with area 8. However, when some minutes later the saturation problem is also

detected in area 11, Atena and Demetra_s best signal proposals happen to be mutually exclusive,

since they try to display different messages on the same VMS panel, i.e. panel P3. This conflict is

resolved locally by each CA, applying conventions respecting priorities for the use of the VMS

panels, based on the severity of the problems and the time of the day when these problems

occur. Finally, both CAs send potential signal plans for their control zones to the UIA, which

displays them to the DM as a coherent global action proposal. In this example, the severity of the

problems to be solved by Atena is initially higher than the severity of the new congestion in area

11. Therefore, Atena and Demetra agree that panel P3 should be used by Atena. Note that thirty

minutes later the opposite may occur, and Demetra will use P3 to divert traffic from area 11.

Conclusion All along, we have seen the creation of different models of DSS by the three set of authors

analyzed in this study and how they have all been able to use DSS as a tool to provide solution to

traffic management problems in major urban areas across the globe. DSS has proven to be a very

efficient tool based on the models that were developed and it has been seen to be a tool that

provides solution to the traffic problems deciding the points where there is need to widen lanes,

provide traffic personnel, and stiffen traffic rules amongst other possible revealed solutions.

References

Almejalli, K., Dahal, K., & Hossain, A. (2006). Road Traffic Decision Support System. Bradford.

Boyce, D. E., & Bar-Gera, H. (2004). Multiclass Combined Models for Urban Travel Forecasting.

Networks and Spatial Economics, Vol. 4, 115-124.

Cuena J., J., & Ossowski, S. (1999). Distributed Models for Decision Support in Multi Agent

Systems. A Modern Approach to DAI, ed. Weib, Cambridge, MA: The MIT Press., 459-504.

De Cea, J. J., Fernandez, L., Dekock, V., & Soto, A. O. (2002). ESTRAUS: A Computer Package For

Solving Supply Demand Equilibrium Problems on Multimodal Trban Transportation

Networks With Multiple User Classes. Santiago: Unpublished.

Hasan, M. K. (2010). A Framework for Intelligent Decision Support System for Traffic Congestion

Management System. Scientific Research, 270-289.

Lerebourg, S., Dutot, A., Bertelle, C., & Olivier, D. (2002). Road Traffic Management Based on Ant

System and Regulation Model.

Ossowski, S., Hernandez, J. Z., Belmonte, M.-V., Alberto, F., Garcıa-Serrano, A., Perez-de-la-Cruz,

J.-L., . . . Triguero, F. (2005). Decision support for traffic management based on

organisational and communicative multiagent abstractions. Elsevier, 272-298.