TraMas: Traffic Control through Behaviour-based Multi-Agent System1 José M. Fernandes INESC - Aveiro Campus Universitário de Santiago 3810 Aveiro, Portugal
[email protected]
Eugénio Oliveira Faculty of Engineering, Univ.of Porto Rua dos Bragas, 4099 Porto Codex, Portugal
[email protected]
Abstract Car traffic management is a complex problem that can be seen as a good example of an inherent distributed problem. The most popular approach to solve this problem is to rely on flexible control of traffic light systems. In this paper we focus our attention in Tramas, our traffic management system based on a reactive agents’ strategy for controlling traffic light systems. Results extracted from several simulated scenarios show that our approach compares favourably with more static strategies.
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INTRODUCTION
Several different AI-based strategies have been proposed, in the past, for the traffic management problem [2]. Our approach to traffic management is based in an homogeneous Multi-Agent System (MAS) (mapping each crossroad into a separated agent). The agent’s design is intended to accommodate strategies ranging from the more simple reactive to more sophisticated cognitive ones.
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THE TRAFFIC CONTROLLER
Currently, we are testing agents following a more reactive model, based on a subsumption architecture [3] with three different levels: a traffic light controller, a local decision maker and a cooperative level which may send and/or receive requests for traffic light changes (besides sharing local information). An agent requests others for help (switching to green or red in a convenient direction) whenever, locally, it can not avoid traffic jams (high density traffic). Car
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Figure 1 –Class model of the Tramas system. The Manager is the “traffic control” agent
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This paper is part the work was partially funded by grant PRAXIS XII/BIC/12254/96 of “Fundação para a Ciência e Tecnologia”, Portugal.
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ENVIROMENT
The environment used to evaluate the system was computer simulated. The traffic models follow the one described in [5] based on cellular automata for car motion and in [6], for generating traffic into the system. The framework was implemented as a distributed system based on Java [1] and on Voyager [7]. Voyager is a Corba compliant ORB architecture that enables transparent integration of concurrency and distribution in standard Java sequential programming. All the modelling and implementation process was heavily based in object oriented paradigm, specifically in UML [4]. From our perspective, agents were seen as object extensions (Figure 1).
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EVALUATION AND CONCLUSIONS
Several scenarios have been simulated to test different kinds of interdependencies between adjacent crossroads. The evaluation was done over those scenarios based on two main factors: car waiting time at crossroads (quality measure) and average speed (flow efficiency measure). We expected to obtain, with our system, higher car flow without compromising waiting times. There were three different strategies considered: one non agent-based with fixed traffic light timers (static), an agent-based without communication ability (autistic) and finally one based on full feature agents (agent). The latter made use of several simple cooperative agent behaviour rules. After several simulations combining all the scenarios and strategies, we observed that agent-based strategies were more successful in maintaining higher flows of car traffic than static, without compromising the waiting time criteria. It was also possible to see the emergence of coordination patterns between communicating agents, such as green wave patterns, in spite of the lack of initial synchronisation. At this stage it is still difficult to state definitive conclusions, once new scenarios have to be simulated. However it is probable that we might be able to show the significant role that agents’ communication has in the emergent behaviour of the MAS for traffic management control.
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FUTURE WORK
Besides enhancing and enlarging simulation scenarios to evaluate the agent-based traffic control system, our main line of research will be the introduction of adaptive agents. We intend to show how reinforcement learning based agents quick adapt themselves to different traffic scenarios coherently working together to improve the traffic flow over the all system.
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References
[1] Ken Arnold, James Gosling, “The Java Programming Language”, Addison-Wesley, 1998 [2] M. Bielli, G. Ambrosino and M. Boero (Eds.) “Artificial Intelligence Application to Traffic Engineering”, VSP, Utrecht, ISBN 90-6764-171-5, 1994 [3] Rodney A. Brooks, “Intelligence Without Reason”, Proceedings IJCAI-91, 1991 [4] Martin Fowler and Kendall Scott, “UML distilled”, Addison-Wesley, 1997 [5] P. Simon, K. Nagel, “A simplified cellular automaton model for city traffic”, Tech. Report LA-UR 97-707, Los Alamos National Laboratory, 1997 [6] “Traffic Flow Theory, A State of the Art Report - Dratf”, Transportation Research Board - US DOT, (http://www-cta.ornl.gov/cta/research/trb/tft.html ) [7] “Voyager, the agent ORB for Java, Core Technology User Guide Version 1.0.0”, Objectspace, Inc, 1997 ( http://www.objectspace.com/voyager )
