SYSTEM ARCHITECTURE OF THE DECISION SUPPORT
SYSTEM EMPLOYING MICROSCOPIC SIMULATION AND
EXPERT SYSTEM IN PARALLEL FOR THE POST INCIDENT
TRAFFIC MANAGEMENT
S. Akhtar Ali Shah
1
and Hojung Kim
2
1
Institute of Geography, Urban and Regional Planning, University of Peshawar, Peshawar, Pakistan
2
L.G. Electronics, Seoul, Republic of Korea
Keywords: Traffic management center, Micro simulation, Expert system.
Abstract: This paper presents system architecture of the post-incident decision support system (PIDSS), which
incorporates the predicted incident impacts from an offline microscopic simulation platform into an expert
system. The system yields an immediate operational strategy for the freeway managers that can further be
fine-tuned with the online simulation results. The novel idea presented in this paper is the replacement of
the domain expert and knowledge engineer with the output of the microscopic simulation that would make
post incident congestion mitigation on the road network more efficient and cost effective.
1 INTRODUCTION
Non-recurring congestion is the result of traffic
accidents, bad weather, road works or unplanned
special event that disrupts traffic flows and causes
unexpected delays. It abruptly reduces the available
capacity and reliability of the entire transportation
system and thus needs to be intelligently tackled.
.The orthodox approach of tackling the non-
recurring congestion relies heavily on the individual
expertise and experience of the traffic managers who
tend to nominate certain heuristics (or guesswork)
about the impacts of an incident. This approach may
cause time loss and induces inconsistency in the
entire incident management operation resulting in an
inefficient use of resources and uncoordinated
mitigation strategy.
In a post-incident scenario, the problems faced
by the traffic managers at the Traffic Management
Center (TMC) have also been acknowledged by
Nagel and Schreckenberg (1992) and there have
been a significant number of other attempts in this
area by researchers as well as practitioners (Hayashi,
Morisugi, 2000; Yoon, et al., 2008). However initial
algorithms for the incident related congestion
mitigation were mainly based on the analytical
models, knowledge-based expert system and
geographic information system (GIS). (Xaichen and
Daniel, 1995) used cellular automata (CA) model for
the prediction of flows using real-time inductance
loop data for freeway traffic that demonstrates the
viability to integrate inductance loop data, cellular
automata and car-following models to simulate the
traffic dynamics for the prediction of the post
incident traffic flows.
This article imparts the system architecture of a
hybrid solution that is named as post-incident
decision support system (PIDSS) that employs
micro-simulation in conjunction with intelligent
system for the analysis based traffic management.
The cornerstone of the approach is an appreciation
of the real incident scenario that demands an
expeditious decision and the participation of the
local network managers for the optimal effectiveness
and coherence through automation of the whole post
incident decision-making process. The feasibility
aspects and specification of requirements of PIDSS
are discussed along with its ability to work in post-
incident scenario for the efficient functioning of the
freeways as well as the urban road networks.
113
Akhtar Ali Shah S. and Kim H..
SYSTEM ARCHITECTURE OF THE DECISION SUPPORT SYSTEM EMPLOYING MICROSCOPIC SIMULATION AND EXPERT SYSTEM IN
PARALLEL FOR THE POST INCIDENT TRAFFIC MANAGEMENT.
DOI: 10.5220/0003385301130117
In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 113-117
ISBN: 978-989-8425-54-6
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
2 THE CONCEPTUAL
FRAMEWORK OF PIDSS
The conceptual framework of PIDSS is based on
associating the output of the freeway incident
analysis system (FIAS), in the whole process of
traffic management and modifying the mitigatory
measures as per online simulated results (Figure1).
The FIAS, which was developed earlier by the
authors and has been, discussed elsewhere in detail
(Kim et al., 2004 and Shah et al. 2008) employs
historical data supplemented with the real-time data
from the toll collection system (TCS) and the
vehicle detection system (VDS) as well as the spatial
data on micro-simulation platform.
The perception of an incident and its impact
forecasting is the kernel in the whole process of non-
recurrent congestion management and needs to be
consistently measured with a significant degree of
reliability. In this context, findings of the micro
simulator (FIAS) are found useful and can be
injected as a replacement to the traditional heuristics
that are neither consistent nor tangible. As online
simulation needs certain time before displaying the
impacts of an incident and the scenario requires
immediate mitigatory measure, therefore, offline
simulation results are used to devise an immediate
mitigatory plan that shall be refined and updated
once real-time incident impact data is available
(Figure 1).
3 KNOWLEDGE-BASED EXPERT
SYSTEM
In a post-incident scenario, a knowledge-based
expert system (KBES) has been conventionally
Figure 1: A conceptual framework of PIDSS.
recommended because of its potentials to streamline
and automat certain low level procedural tasks and
emulate the experienced traffic manager (Zhang and
Ritchie 1994; Ritchie, 1990; Flippo and Ritchie,
2002). The essence of these systems is the
application of expert’s knowledge in a narrow and
well-defined problem arena. A KBES can employ
symbolic reasoning and heuristics in problem
solving that enhances its suitability for the complex
scenario analysis with deficient algorithmic solution
(Mitrovich, et al., 2006).
Santa Monica Freeway Smart Corridor (Flippo
and Ritchie, 2002) project was one of the very first
attempt in which a full-fledged real-time knowledge-
based expert system was employed for the traffic
surveillance and control purposes. In this work, a
conceptual framework of a multiple real-time
knowledge-based expert system was suggested.
Zhang and Ritchie (2004) propose a knowledge-
based expert system and name it as freeway real-
time expert system demonstration (FRED). Their
proposed methodology is to follow experienced
TMC operators’ and traffic engineers’ approach
using an expert system. It incorporates symbolic
reasoning and heuristics for solving the problem.
This system is capable of network level operations,
multiple incident handling, better user interface and
incident recovery monitoring. Nonetheless, FRED
does not predict the post-incident traffic delays and
the significance of the historic data are also not
realized in the analysis.
In another attempt (op cit.) a knowledge-based
system is employed focusing on the cooperative
inter-jurisdictional traffic management. The system
adopts a multi-decision maker approach that reflects
the spatial and administrative organization of traffic
management agencies in US cities. It provides a
cooperative solution that exploits the willingness of
agencies to cooperate and unify their problem
solving capabilities without compromising their
individual authority and the inherent distribution of
data and expertise.
4 SYSTEM ARCHITECTURE
The indispensable part of modelling is to establish a
logical information exchange between the user and
the system besides in vitro processing and
simulation methods that takes place within the
system. System architecture constitutes interactions
amongst various components of the system for
achieving predefined user objective. The two
elements of system architecture of PIDSS are
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
114
described in greater depth below.
4.1 Logical Architecture
The logical architecture of PIDSS (Figure 2)
primarily considered as a ‘specification of
requirement’ upon which the working model of
deployment guide exists. The main activity flow
diagram is flanked with two fundamental units of the
architecture: FIAS and the Simulation Based Expert
System (SBES). Incident detection (using prevailing
algorithms like speed map) and verification is
followed with the insertion of incident parameters
like type, severity, cross sectional location, time,
number of vehicle involved in the expert system.
The domain of the expert system extends from
incident categorization based on input parameters;
through impact assessment for encapsulating each
individual category of incident; to the application of
different mitigatory measures. These tools are
mostly ITS applications that are primarily operation
and information oriented at local level but may
cause significant travel and traffic impacts at a
regional levels.
Autonomously, managers also instigate FIAS for
the cross reference and any subsequent tuning in the
impact assessment is based on online simulation in
the later stages of operation. SBES also contains a
significantly large data bank of offline coordinated
relative weight of individual measures and its linear
combination for the optimal mitigatory impacts. The
relative weights are derived from the significance of
each measure and its overall performance and
impacts on the operational functioning of the whole
network.
The coordinated initial mitigatory plan is thus
injected into the system that has been locally
optimized and is reviewed for any changes in
consultation with the lower tier of the system
network at local level to avoid any friction from
other stakeholders in the system before effectuation.
This pluralistic approach is essential and highly
recommended to use the full potential of the
physical infrastructure. The system also provides a
real-time monitoring mechanism that suggests
improvement in a running mitigatory plan or any of
its components. This is supplemented with a post-
incident evaluation that helps in the expert system
database evolution.
Incident detection
Verification
Incident parameters
Incident categorization
Impacts assessment
Mitigatory plan
formulation
Plan review and
revision, if needed
Execution of the final
mitigatory plan
Performance
Monitoring
Post incident
Evaluation
Offline
simulation
FIAS
Online
simulation
FIAS predictions of
the impact
Revision
if needed
Local traffic
management center
ES upgrading
ES upgrading
SBES
- Categorization
- Impact assessment
- Control measure assessment
- Coordination module
Incident detection
Verification
Incident parameters
Incident categorization
Impacts assessment
Mitigatory plan
formulation
Plan review and
revision, if needed
Execution of the final
mitigatory plan
Performance
Monitoring
Post incident
Evaluation
Offline
simulation
FIAS
Online
simulation
FIAS predictions of
the impact
Revision
if needed
Local traffic
management center
ES upgrading
ES upgrading
SBES
- Categorization
- Impact assessment
- Control measure assessment
- Coordination module
Figure 2: The logical framework of PIDSS.
4.2 Physical Architecture
The physical architecture of PIDSS (Figure 3)
addresses organization of the system on the
functional lines which is based on information flow
mapping and logical modeling. It supports a range of
evaluation conditions and effectuation strategies in
terms of standardized framework of the logical
architecture. The architecture generates information
in a format that is more tangible and can invoke a
locally optimized and globally integrated mitigatory
plan at its terminal point.
Traffic managers are placed at the crux level and
have interfacing with every internal subsystem and
execute a control over all data flow. The essential
architectural flows in the key logical units like
incident categorization; impact assessment or
strategy coordination with the local traffic mangers
encapsulates huge data sharing and information
exchange between the terminal point and the
subsystem. The manger, who has access to the
essential ITS infrastructure like the surveillance
system; verification infrastructure; FTMS and
highway geographic information system (HGIS)
server, injects incident parameters into the Expert
System
4.3 Simulation based Expert System
Collecting knowledge needed to solve problems and
build the knowledge base continues to be the biggest
bottleneck in building expert systems. This
SYSTEM ARCHITECTURE OF THE DECISION SUPPORT SYSTEM EMPLOYING MICROSCOPIC SIMULATION
AND EXPERT SYSTEM IN PARALLEL FOR THE POST INCIDENT TRAFFIC MANAGEMENT
115
impedance is resolved in this hybrid system by
replacing an expert domain of a conventional expert
system with the microscopic simulation platform
trained in knowledge acquisition and representation
into a dependency network. The high level structure
around the fundamental activities of the system
defined in the logical architecture is realized in an
orthodox transport planning paradigm with two
typical modules: analysis and intervention. The
analysis stage is further sub classified into main
categories incident categorization and impact
assessment module. The intervention stage
encompasses coordination (with the local agency)
module and mitigatory measure module. The key
modules of the stages are discussed below.
4.3.1 Incident Categorization Module
This is an ES tool that provides a representation
scheme for incident categorization expressing
knowledge about parameters and categories.
Parameters include location, type and severity,
number of vehicles involved, critical section and
capacity reduction, estimated duration, and location
of the nearest rescue infrastructure. These
parameters classify the incident into previously
defined categories as per type and combination of
parameters.
4.3.2 Impact Assessment Module
This module is one of the indispensable components
of the proposed expert system. It replaces the
conventional knowledge base with both off and on-
line versions of FIAS impacts of all predefined
categories, which are assessed and tabulated as a
knowledge base. The FIAS off line simulation
module is a major component in the knowledge base
that encapsulates all categories of an incident for
impacts prediction and subsequent indexing. On-
line FIAS predictions bring dynamism in the system
with the supplementation of real-time scenario.
4.3.3 Coordination Module
Several research development and deployment
projects have acknowledged the importance of
multi-agent, coordinated and inter-jurisdictional
approach that was reported by Flippo and Ritchie,
(2002). The multi-decision making in PIDSS
simplifies the scenario by executing a knowledge
base for the priorities of the local authority of the
abutting road network regarding different mitigatory
measures and its diversity with the many other
parameters like category, type and location of the
incident and its impacts on the general flows in the
area. These knowledge bases shall be reviewed
periodically and updated by assigning the revised
priority through relative weighting system approach.
Besides the real-time participation of the local
agencies is also insured using a fast TCP/IP based
communication protocol.
4.3.4 Mitigatory Plan Formulation Module
Mitigatory plan can be classified into two groups:
Preliminary and revised. The preliminary plan
originates from the knowledge-based relative
weighting system of individual mitigatory operation
selected by the respective stakeholders from the
local areas as well as the freeway agencies. However
the dynamism of the system allows for any real-time
incorporation of the changes in the values of the
relative weighting depending upon the revised
priorities for any local reason like time of the day,
conditions of the network and so on. These revisions
are amalgamated into the plan, which will be
regarded as the revised plan and executed. The real-
time coordination with the local agents allows for
the optimization and globalization of a solution
using the full potentials of the network as a unit.
Local area
traffic manager
Rescue
infrastructure
Traffic manager
Surveillance
system
Verification
system
Incident
Parameter
FIAS
FTMS server
HGIS
SBES
Incident categorization
module
Impact assessment
module
Coordination
module
Mitigatory
module
Incident parameters
Categorization
knowledge bank
Impacts on local traffic
Capacity of the local
network
Off-line FIAS operation
and building of
knowledgebase for
impacts
Impacts of individual
measures
Combined impact
Selection of the
optimum combination
FIAS online
simulation and
prediction of impacts
Comparison with the
database impact and
fine turning if needed
Execution of the
selected optimum
mitagory plan
Monitoring and fine
turning if needed
Local area
traffic manager
Rescue
infrastructure
Traffic manager
Surveillance
system
Verification
system
Incident
Parameter
FIAS
FTMS server
HGIS
Traffic manager
Surveillance
system
Verification
system
Incident
Parameter
FIAS
FTMS server
HGIS
SBES
Incident categorization
module
Impact assessment
module
Coordination
module
Mitigatory
module
Incident parameters
Categorization
knowledge bank
Impacts on local traffic
Capacity of the local
network
Off-line FIAS operation
and building of
knowledgebase for
impacts
Impacts of individual
measures
Combined impact
Selection of the
optimum combination
FIAS online
simulation and
prediction of impacts
Comparison with the
database impact and
fine turning if needed
FIAS online
simulation and
prediction of impacts
Comparison with the
database impact and
fine turning if needed
Execution of the
selected optimum
mitagory plan
Monitoring and fine
turning if needed
Execution of the
selected optimum
mitagory plan
Monitoring and fine
turning if needed
Figure 3: Physical architecture of PIDSS.
5 DISCUSSIONS AND
CONCLUSIONS
This paper discusses an application of transport
telematics to automate decision support for the TMC
personnel in the post-incident scenario using a
hybrid system of microscopic simulation and
artificial intelligence. The unique feature of PIDSS
is the immediate decision making in the crises state
incorporating offline simulation results used to
instigate a preliminary mitigatory plan. The tool
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
116
facilitates local integration for a regional solution
incorporating the predefined interest of all key
stakeholders employing relative weighting
algorithm, which is rapidly coordinated and updated
using IT infrastructure. This initial plan is revised
with the FIAS online simulation for incidents
impacts prediction that incorporates real data with
historical and spatial data in the incident analysis
stage. The flexibility of the tools allows for the
incorporation of the priority revision of all
stakeholders with mutual understanding and
collaboration.
In the development of PIDSS, unlike some of the
known algorithms (Zhang and Ritchie 1994; Ritchie,
1990; Flippo and Ritchie, 2002), a different
approach of the knowledge acquisition was opted.
The domain expert was replaced with the
microscopic simulation platform, trained in
knowledge acquisition and representation. Using a
data manipulation algorithm the outputs of
simulation are transformed into dependency
networks (an outline of the rules), which is
subsequently coded and programmed into the
system. Thus the simulation replaces both the expert
domain (the source of knowledge) and the designers
of the expert system (knowledge engineer). The
most obvious advantage of this development method
is its cost effectiveness to build expert systems to
eliminate the need for an expert domain and the
knowledge engineer for the extraction and
representation of knowledge. Nonetheless the crucial
advantage is the speed, coordination and time saving
in a crisis scenario.
PIDSS knowledge base is rooted into a
microscopic simulation based model that predicts
the post-incident traffic impacts, which is imperative
for the real-time incident analysis and improves the
functioning of TMC. It is anticipated that the
incident analysis result in this format will help the
traffic managers to take significantly consistent steps
based on tangible information and not the
speculative approach.
ACKNOWLEDGEMENTS
Part of this paper was developed in collaboration
with the Korea Highway Corporation (KHC).
However its content reflects views of the authors; it
neither constitutes a standard, specification or
regulation nor official views or policy of the KHC.
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AND EXPERT SYSTEM IN PARALLEL FOR THE POST INCIDENT TRAFFIC MANAGEMENT
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