05-07-2012, 09:45 AM
A Review of the Applications of Agent Technology in Traffic and Transportation Systems
[attachment=26750]
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.
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.
ARCHITECTURE AND PLATFORMS OF AGENT-BASED
TRAFFIC CONTROL AND MANAGEMENT SYSTEMS
The operation of agents is supported and managed by distributed
software platforms known as agent systems. The name
of MASs usually refers to systems that support stationary
agents, and mobile agent systems support mobile agents. An
agent system provides mechanisms for agent management,
agent communication, and agent directory maintenance. A mobile
agent system provides additional mechanisms to support
the migration and execution of mobile agents. In an agent
system, agencies are the major building blocks and are installed
in each node of a networked system, in which agents
reside and execute. To facilitate the interoperation of agents
and agent systems across heterogeneous agent platforms, agencies
designed to comply with agent standards are highly
desired.
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.
AGENT-BASED SYSTEMS FOR AIR-TRAFFIC
CONTROL AND MANAGEMENT
The geographical and functional distribution and the highly
dynamic nature of air traffic control (ATC) make it an ideal
candidate with many potential applications that can be modeled
with MAS [65], such as collision avoidance [66], [67] and air
traffic flow management [68]. The optimal aircraft sequencing
using intelligent scheduling (OASIS) presented in [69] is a
real-time agent-oriented system developed to support air traffic
management. OASIS distributes air-traffic-management tasks
into two classes of autonomous and cooperating agents: aircraft
agents and global agents. Each aircraft agent associates with
an arriving aircraft and performs computation or reasoning
relevant to the aircraft. The global agents, including Coordinator,
Sequencer, Trajectory Checker, Wind Model, and User
Interface, handle interaircraft coordination and reasoning.
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.
DISCUSSIONS
In this section, we discuss the following issues: system
interoperability, the ability to handle uncertainty, and system
extensibility, to share our visions of future research directions.
First, interoperability is critically needed in making decisions
based on information across systems, organizational and jurisdictional
boundaries, or application scenarios in which the
integration of multiple agent systems is needed [124]. To tackle
the interoperability issue, IEEE FIPA, which is a consortium
of companies, government agencies.
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.
[attachment=26750]
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.
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.
ARCHITECTURE AND PLATFORMS OF AGENT-BASED
TRAFFIC CONTROL AND MANAGEMENT SYSTEMS
The operation of agents is supported and managed by distributed
software platforms known as agent systems. The name
of MASs usually refers to systems that support stationary
agents, and mobile agent systems support mobile agents. An
agent system provides mechanisms for agent management,
agent communication, and agent directory maintenance. A mobile
agent system provides additional mechanisms to support
the migration and execution of mobile agents. In an agent
system, agencies are the major building blocks and are installed
in each node of a networked system, in which agents
reside and execute. To facilitate the interoperation of agents
and agent systems across heterogeneous agent platforms, agencies
designed to comply with agent standards are highly
desired.
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.
AGENT-BASED SYSTEMS FOR AIR-TRAFFIC
CONTROL AND MANAGEMENT
The geographical and functional distribution and the highly
dynamic nature of air traffic control (ATC) make it an ideal
candidate with many potential applications that can be modeled
with MAS [65], such as collision avoidance [66], [67] and air
traffic flow management [68]. The optimal aircraft sequencing
using intelligent scheduling (OASIS) presented in [69] is a
real-time agent-oriented system developed to support air traffic
management. OASIS distributes air-traffic-management tasks
into two classes of autonomous and cooperating agents: aircraft
agents and global agents. Each aircraft agent associates with
an arriving aircraft and performs computation or reasoning
relevant to the aircraft. The global agents, including Coordinator,
Sequencer, Trajectory Checker, Wind Model, and User
Interface, handle interaircraft coordination and reasoning.
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.
DISCUSSIONS
In this section, we discuss the following issues: system
interoperability, the ability to handle uncertainty, and system
extensibility, to share our visions of future research directions.
First, interoperability is critically needed in making decisions
based on information across systems, organizational and jurisdictional
boundaries, or application scenarios in which the
integration of multiple agent systems is needed [124]. To tackle
the interoperability issue, IEEE FIPA, which is a consortium
of companies, government agencies.
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.