15-12-2012, 05:07 PM
A Review of the Applications of Agent Technology in Traffic and Transportation Systems
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Abstract
The agent computing paradigm is rapidly emerging
as one of the powerful technologies for the development of largescale
distributed systems to deal with the uncertainty in a dynamic
environment. The domain of traffic and transportation systems is
well suited for an agent-based approach because transportation
systems are usually geographically distributed in dynamic changing
environments. Our literature survey shows that the techniques
and methods resulting from the field of agent and multiagent systems
have been applied to many aspects of traffic and transportation
systems, including modeling and simulation, dynamic routing
and congestion management, and intelligent traffic control. This
paper examines an agent-based approach and its applications in
different modes of transportation, including roadway, railway, and
air transportation. This paper also addresses some critical issues in
developing agent-based traffic control and management systems,
such as interoperability, flexibility, and extendibility. Finally, several
future research directions toward the successful deployment
of agent technology in traffic and transportation systems are
discussed.
INTRODUCTION
AGENT-BASED computing is one of the powerful technologies
for the development of distributed complex systems
[1]. Many researchers believe that agents represent the
most important new paradigm for software development since
object-oriented design [2], and the concept of intelligent agents
has already found a diverse range of applications in manufacturing,
real-time control systems, electronic commerce, network
management, transportation systems, information management,
scientific computing, health care, and entertainment. The reason
for the growing success of agent technology in these areas is
that the inherent distribution allows for a natural decomposition
of the system into multiple agents that interact with each other
to achieve a desired global goal. The agent technology can
significantly enhance the design and analysis of problem domains
under the following three conditions [3]: 1) The problem
domain is geographically distributed; 2) the subsystems exist in
a dynamic environment; and 3) the subsystems need to interact
with each other more flexibly.
AGENT-BASED SYSTEMS FOR ROADWAY TRANSPORTATION
Major challenges that roadway transportation faces are increasing
traffic congestion, accidents, transportation delays,
and vehicle emissions. The Texas Transportation Institute and
the Texas A&M University System 2009 Urban Mobility Report
[20] presents detailed trend data from 1982 to 2007 for
439 urban areas in U.S. The report provides both local view
and national perspective on the growth and extent of traffic
congestion. According to the report, congestion costs (the cost
of extra time and fuel) in 439 urban areas are increasing from
$16.7 billion in 1982 to $87.2 billion in 2007. To address the
current problems and meet the growing travel demand, the
solution is either constructing additional conventional roadway
infrastructure or applying new technology to efficiently
and effectively use existing infrastructure [21]. It is widely
recognized, however, that the opportunities for building new
physical infrastructure are decreasing because of increasing
cost, environmental impact, and space limitations.
Freeway Traffic Management
Logi and Ritchie [23] investigate the interjurisdictional traffic
congestion management on freeway and surface street (arterial)
networks. Their system is composed of two interacting realtime
decision support agents, i.e., a freeway agent and an
arterial agent, for analysis of congestion and for generation of
suitable responses. The freeway agent supports incident management
operations for a freeway subnetwork, and the arterial
agent supports operation for the adjacent arterial network. Both
agents continuously receive real-time traffic data, incidentdetection
data, and control status of the control devices on
the network (signals, ramp meters, and changeable message
signs). By performing an analysis of the input data and interacting
with a human operator at their local traffic operation
center (TOC), each agent generates suitable local control plans,
which are aimed at reducing the impact of congestion at a
local level. The system provides a dialog facility through a
distributed user interface to allow operators at different TOCs
to agree on the selection of a global solution. Van Katwijk and
Van Koningsbruggen [24] propose an agent-based approach for
the cooperation of traffic-control and management instruments.
To improve traffic flow and provide safe and secure transport
of people and goods, increasingly more traffic control and management
instruments are installed on highways.
MULTIAGENT TRAFFIC MODELING AND SIMULATION
Traffic and transportation systems consist of many autonomous
and intelligent entities, such as man-driven vehicles,
signal lights, and variable signs, which are distributed over
a large area and interact with each other to achieve certain
transportation goals. MASs provide a suitable way to model
and simulate traffic systems since they offer an intuitive way
to describe every autonomous entity on the individual level. In
a multiagent traffic-simulation system, each intelligent traffic
entity is modeled as an agent. Agents can work cooperatively
with each other. MASs have been widely used to investigate
traffic-related problems, such as route guidance, urban traffic
management and control (UTMC), collaborative driving [88],
[89], railway traffic control [90], combined rail/road transport
[91], ATC [92], [93], and the optimization of airport operation
[94]. Table III lists some of the multiagent-based traffic modeling
and simulation applications.
CONCLUSION
Software agents and their applications in traffic and transportation
systems have been studied for over one decade. A
number of agent-based applications have already been reported
in the literature. These applications propose and investigate
different agent-based approaches in various traffic and transportation
related areas. The research results clearly demonstrate
the potential of using agent technology to improve the
performance of traffic and transportation systems. Most agentbased
applications, however, focus on modeling and simulation.
Few real-world applications are implemented and deployed. In
general, the design, implementation, and application of agentbased
approaches in the area of traffic and transportation are
still immature and need to be further studied. The integration of
new technologies, such as mobile agent technology, should be
considered to enhance the flexibility of systems and the ability
to deal with uncertainty in dynamic environments.