2. field. From multiagent systems and
agent structure to ways of negotiating
Detector Detector Detector between agents to control agent strat-
egies, all these fields have had varying
Traffic management systems in degrees of success.
Mainframe (1940s–1950s) centralized model (1960s) Now, the IT industry has ushered
in the fifth computing paradigm:
cloud computing. Based on the In-
ternet, cloud computing provides on-
demand computing capacity to indi-
Detector
Traffic management systems
viduals and businesses in the form of
PC (1960s) with single points (1970s–1980s) heterogeneous and autonomous ser-
vices. With cloud computing, users
do not need to understand the details
Organizer
of the infrastructure in the “clouds;”
they need only know what resources
Coordinator Coordinator Coordinator
they need and how to obtain appro-
priate services, which shields the
computational complexity of provid-
Executor
Executor
Executor
Executor
Executor
Executor
ing the required services.
In recent years, the research and
Traffic management systems application of parallel transportation
LAN (1970s) in distributed model (1980s–1990s) management systems (PtMS), which
consists of artificial systems, com-
Mobile agent putational experiments, and parallel
execution, has become a hot spot in
the traffic research field.2,3 Here, the
Internet term parallel describes the parallel
interaction between an actual trans-
portation system and one or more of
its corresponding artificial or virtual
Traffic management systems counterparts.4
Internet (1980s–1990s) based on agents (2000s) Such complex systems make it diffi-
cult or even impossible to build accu-
Mobile agent
rate models and perform experiments,
ATS so PtMSs use artificial transportation
systems (ATS) to compensate for this
defect. Moreover, ATSs also help op-
timize and evaluate large amounts of
traffic-control strategies. Cloud com-
Actual puting caters to the idea of “local
transportation
systems simple, remote complex” in parallel
Cloud computing (2000s) Parallel transportation management systems (2010s) traffic systems. Such systems can take
Figure 1. Relationship between the shifts in the computing and traffic management
advantage of cloud computing to or-
paradigms. ganize computing experiments, test
the performance of different traffic
agents were proposed to handle this to improve performance by reduc- strategies, and so on. Thus, only the
vexing problem. Only requiring a ing communication time and costs. optimum traffic strategies will be used
runtime environment, mobile agents This computing paradigm soon drew in urban-traffic control and manage-
can run computations near data much attention in the transportation ment systems. This helps enhance
74 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS
3. urban-traffic management system Traffic-strategy
Ag
performance and minimizes the sys-
e
developers
te c
nt
ra fi
es
st Traf
pe
gi
r fo
tem’s hardware requirements to ac-
rm
an
celerate the popularization of parallel
ce
Agent performance
traffic systems.
Agent prototype
Agent-Based Traffic Typical traffic
Management Systems Traffic-strategy
database
Traffic-strategy scenes testbed
agent database
Agent technology was used in traffic-
Agent storage and
management systems as early as generation module Agent testing and training module
1992, while multiagent traffic-
performance
en f
Ask agent
ag n o
to move
ts
Agent
ffic atio
management systems were presented
tra bin
m
later.5 However, all these systems fo-
Co
cus on negotiation and collaboration
Database of
between static agents for coordina- te
of knowledge age Mod
nt- i
sta dis fied
tion and optimization. 6–8 In 2004, nd n t
ma ribut
k a tio
tas orta tio
n p ion
mobile agent technology began to at- ffic ansp ibu
Tra tr str
-di ap
tract the attention of the transpor- ent m
Ag
tation field. The characteristics of
mobile agents—autonomous, mobile, Traffic data
and adaptive—make them suitable
to handling the uncertainties and in-
constant states in a dynamic envi-
ronment.9 The mobile agent moves
through the network to reach con- Agent deployment and support module
trol devices and implements appropri- Actual traffic management system Artificial transportation system
ate strategies in either autonomous or
Figure 2. Overview of the main functional elements in the organization layer of
passive modes. In this way, traffic de-
Adapts.
vices only need to provide an operat-
ing platform for mobile traffic agents
working in dynamic environments, as the carrier of the control strategies management agent (MA), three data-
without having to contain every traf- in the system. bases (control strategy, typical traffic
fic strategies. This approach saves In the follow-up articles, both the scenes, and traffic strategy agent), and
storage and computing capacity in architecture and the function of mo- an artificial transportation system. As
physical control devices, which helps bile traffic control agents were defined one traffic strategy has been proposed,
reduce their update and replacement clearly. The static agents in each layer the strategy code is saved in the traffic
rates. Moreover, when faced with the were also depicted in detail. What’s strategy database. Then, according to
different requirements of dynamic more, a new traffic signal controller the agent’s prototype, the traffic strat-
traffic scenes, a multiagent system was designed to provide the runtime egy will be encapsulated into a traffic
taking advantage of mobile agents environment for mobile agent. strategy agent that is saved in the traf-
will perform better than any static Currently, Adapts is part of PtMS, fic strategy agent database. Also, the
agent system. which can take advantage of mobile traffic strategy agent will be tested by
In 2005, the Agent-Based Dis- traffic strategy agents to manage a the typical traffic scenes to review its
tributed and Adaptive Platforms for road map. The organization layer, performance. Typical traffic scenes,
Transportation Systems (Adapts) was which is the core of our system, has which are stored in a typical intersec-
proposed as an hierarchical urban- four functions: agent-oriented task tions database, can determine the per-
traffic-management system.10 The decomposition, agent scheduling, formance of various agents. With the
three layers in Adapts are organiza- encapsulating traffic strategy, and support of the three databases, the
tion, coordination, and execution, re- agent management (see Figure 2). MA embodies the organization layer’s
spectively. Mobile agents play a role The organization layer consists of a intelligence.
January/February 2011 www.computer.org/intelligent 75
4. 1,200
1,000
The function of the requires traffic control,
Evaluation time (s)
800
agents’ scheduling and detection, guidance, mon-
agent-oriented task de- 600 itoring, and emergency
composition is based on subsystems. To handle
the MA’s knowledge 400 the different states in a
base, which consists of 200 traffic environment, an
the performances of dif- urban-traffic management
ferent agents in various 0 system must provide ap-
2 4 6 8 10 12 14 16 18 20
traffic scenes. If the ur- propriate traffic strategy
Number of intersections
ban management system agents. And to handle
cannot deal with a trans- performance improve-
Figure 3. The time required to run ATS and Adapts experiments
portation scene with its on one PC. ments and the addition
existing agents, it will of new subsystems, new
send a traffic task to the traffic strategies must be
organization layer for help. The traf- communicate with ATS to get traffic- introduced continually. So future
fic task contains the information detection data and send back lamp- urban-traffic management systems
about the state of urban transporta- control data. Both running load must generate, store, manage, test,
tion, so a traffic task can be decom- and communication volumes increase optimize, and effectively use a large
posed into a combination of several with the number of intersections. number of mobile agents. Moreover,
typical traffic scenes. With knowl- If the time to complete the experi- they need a decision-support system
edge about the most appropriate traf- mental evaluation exceeds a certain to communicate with traffic manag-
fic strategy agent to deal with any threshold, the experimental results ers. A comprehensive, powerful deci-
typical traffic scene, when the or- become meaningless and useless. As sion-support system with a friendly
ganization layer receives the traffic a result, the carry capacity for ex- human-computer interface is an in-
task, the MA will return a combina- perimental evaluation of one PC is evitable trend in the development of
tion of agents and a map about the limited. urban-traffic management systems.
distribution of agents to solve it. This In our test, we used a 2.66-GHz Thus, future systems must have the
way, this system takes advantage PC with a 1-Gbyte memory to run following capabilities.
of the strategy agent to manage a both ATS and Adapts. The experi-
road map. ment took 3,600 seconds in real Computing Power
Lastly, we set up an ATS to test per- time. The number of intersections The more typical traffic scenes used to
formance of the urban-traffic man- we tested increased from two to 20, test a traffic-strategy agent, the more
agement system based on the map and Figure 3 shows the time cost of detailed the learning about the advan-
showing the distribution of agents. each experiment. When the num- tages and disadvantages of different
ATS is modeled from the bottom up, ber of traffic-control agents is 20, traffic strategy agents will be. In this
and it mirrors the real urban trans- the experiment takes 1,130 seconds. case, the initial agent-distribution
portation environment.11 Because the If we set the time threshold to 600 map will be more accurate. To achieve
speed of the computational experi- seconds, the maximum number of this superior performance, however,
ments is faster than the real world, if intersections in one experiment is testing a large amount of typical traf-
the performance is unsatisfactory, the only 12. This is insufficient to handle fic scenes requires enormous comput-
agent-distribution map in both sys- model major urban areas such as Bei- ing resources.
tems will be modified. jing, where the central area within Researchers have developed many
the Second Ring Road intersection traffic strategies based on AI. Some
New Challenges contains up to 119 intersections. We of them such as neural networks con-
During the runtime of Adapts, we would need several PCs or a high- sume a lot of computing resources for
need to send the agent-distribution performance server to handle the ex- training in order to achieve satisfac-
map and the relevant agents to ATS perimental scale of several hundreds tory performance. However, if a traf-
for experimental evaluation, so we of intersections. fic strategy trains on actuator, the ac-
tested the cost of this operation. In Furthermore, a complete urban- tuator’s limited computing power and
our test, traffic-control agents must traffic management system also inconstant traffic scene will damage
76 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS
5. the performance of the traffic AI Intelligent traffic clouds
agent. As a result, the whole system’s
performance will deteriorate. If the
traffic AI agent is trained before mov-
ing it to the actuator, however, it can
better serve the traffic management
system.
Rational traffic decisions and dis-
tributions of agents need the support Traffic participators
of ATS, which primarily use agent- Traffic managers
oriented programming technology.
Agents themselves can be humans,
vehicles, and so on. To ensure ATS
mirrors real urban transportation,
we need large computing resources to
Traffic-strategy developers Urban-traffic
run many agents. management systems
Figure 4. Overview of urban-traffic management systems based on cloud
Storage computing.
Vast amounts of traffic data such as
the configuration of traffic scenes,
regulations, and information of dif- urban-traffic systems at the time. clouds. The clouds’ customers such
ferent types of agents in ATS need This way we can make full use of as the urban-traffic management sys-
vast amounts of storage. Similarly, existing cheap servers and minimize tems and traffic participants exist
numerous traffic strategy agents and the upfront investment of an entire outside the cloud.
relative information such as control system. Figure 4 gives an overview of urban-
performances about agents under dif- traffic management systems based
ferent traffic scenes also consume a Intelligent Traffic Clouds on cloud computing. The intelligent
lot of storage resources. Finally, the We propose urban-traffic manage- traffic clouds could provide traffic-
decision-support system requires vast ment systems using intelligent traffic strategy agents and agent-distribution
amounts of data about the state of clouds to overcome the issues we’ve maps to the traffic management sys-
urban transportation. described so far. With the support tems, traffic-strategy performance to
Two solutions can help fulfill these of cloud computing technologies, the traffic-strategy developer, and the
requirements: it will go far beyond other multi gent
a state of urban traffic transportation
traffic management systems, ad- and the effect of traffic decisions to
• quip all centers of urban-traffic
E dressing issues such as infinite sys- the traffic managers. It could also
management systems with a super- tem scalability, an appropriate agent deal with different customers’ re-
computer. management scheme, reducing the quests for services such as storage
• Use cloud computing technologies— upfront investment and risk for us- service for traffic data and strategies,
such as Google’s Map-Reduce, ers, and minimizing the total cost of mobile traffic-strategy agents, and
IBM’s Blue Cloud, and Ama- ownership. so on.
zon’s EC2—to construct intelli- With the development of intelli-
gent traffic clouds to serve urban Prototype gent traffic clouds, numerous traffic
transportation. Urban-traffic management systems management systems could connect
based on cloud computing have two and share the clouds’ infinite capabil-
The former both wastes social re- roles: service provider and customer. ity, thus saving resources. Moreover,
sources and risks insufficient capac- All the service providers such as the new traffic strategies can be trans-
ity in the future. On the contrary, the test bed of typical traffic scenes, ATS, formed into mobile agents so such
latter takes advantage of the infinite traffic strategy data ase, and traffic
b systems can continuously improve
scalability of cloud computing to dy- strategy agent database are all veiled with the development of transporta-
namically satisfy the needs of several in the systems’ core: intelligent traffic tion science.
January/February 2011 www.computer.org/intelligent 77
6. Application layer
Standard application
Standard interface
programming interface
Agent-generation service
of urban-traffic management sys-
Agent-oriented task Traffic decision
decomposition service testing service tems, support the running of agents
Agent-management service and ATS test beds, and efficiently
store traffic strategy agents and their
Agent-testing service performances.
Services
Acknowledgments
Platform layer We would like express our apprecia-
Artificial traffic systems tion to Fei-Yue Wang for his guidance and
Services encouragement. This work was supported
3D editor Population generator Weather simulator Path planner in part by NSFC 70890084, 60921061,
90924302 and 90920305.
Services
VM VM VM VM Unified source layer
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ZhenJiang Li is an assistant professor at
the Key Laboratory of Complex Systems
and Intelligence Science, Chinese Academy
of Sciences. Contact him at lzjwhb@gmail.
com.
Cheng Chen is a PhD student at the Key
Laboratory of Complex Systems and In-
telligence Science, Chinese Academy of
Sciences. Contact him at chengchen.cas@
gmail.com.
Kai Wang is a PhD student at the College of
Mechatronics Engineering and Automation,
the National University of Defense Technol-
ogy (NUDT), Changsha, Hunan, China.
Contact him at yogikai@163.com.
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