Abstract
This paper presents a distributed learning control strategy for traffic signals. The strategy uses a fully distributed architecture in which there is effectively only one (low) level of control. Such strategy is aimed at incorporating computational intelligence techniques into the control system to increase the response time of the controller. The idea is implemented by employing learning classifier systems and TCP/IP based communication server, which supports the communication service in the control system. Simulation results in a simplified traffic network show that the control strategy can determine useful control rules within the dynamic traffic environment, and thus improve the traffic conditions.
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References
Robertson, D. I.: TRANSYT— A traffic network study tool. Transport and Research Laboratory, Crowthorne, England (1969)
Luk, J. Y., Sims, A. G. and Lowrie, P. R.: SCATS application and field comparison with TRANSYT optimized fixed time system. In Proc. IEE Int. Conf. Road Traffic Signalling, London (1982)
Lowrie, P. R.: The Sydney coordinated adaptive Traffic system. In Proc. IEE Int. Conf. Road Traffic Signalling, London (1982)
Hunt, P. B., Robertson, D. I., Bretherton, R. D. and Winston, R. I.: SCOOT—A Traffic responsive method of co-ordinating Traffic signals. Transport and Research Laboratory, Crowthorne, England (1982)
Scemama, G.: Traffic control practices in urban areas. Ann. Rev. Report of the Natl Res. Inst. on Transport and Safety. Paris, France (1990)
Al-Khalili, A. J.: Urban Traffic control — a general approach. IEEE Trans. on Syst. Man and Cyber. 15, (1985) 260–271
Montana, D. J. and Czerwinski, S.: Evolving control laws for a network of Traffic signals. Proc. of 1st Annual Conf. on Genetic Programming, (1996) 333–338
Koza, J. R: Genetic Programming. MIT Press, Cambridge, MA (1992)
Cao, Y. J., Ireson, N. I., Bull, L. and Miles, R.: Design of Traffic Junction Controller Using a Classifier System and Fuzzy Logic. In Computational Intelligence: Theory and Applications, Reusch, B. (ed), Lecture Notes in Computer Sciences, 1625, Springer Verlag, (1999) 342–353
Cao, Y. J. and Wu, Q. H.: An improved evolutionary programming approach to economic dispatch. International Journal of Engineering Intelligent Systems, 6,(2), (1998) 187–194
Cao, Y. J. and Wu, Q. H.: Optimisation of control parameters in genetic algorithms: a stochastic approach. International Journal of Systems Science, 20,(2), (1999) 551–559
Mikami, S. and Kakazu, K.: Genetic reinforcement learning for cooperative traffic signal control. Proceedings of the IEEE World Congress on Computational Intelligence, (1994) 223–229
Escazut, C. and Fogarty, T. C.: Coevolving classifier systems to control traffic signals. In Koza, J. R (ed): Late breaking papers at the Genetic Programming 1997 Conference, Stanford University, (1997) 51–56
Holland, J. H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA (1992)
Wilson, S. W.: ZCS: A zeroth level classifier system. Evolutionary Computation, 2, (1994) 1–18
Cao, Y. J. and Wu, Q. H.: A mixed-variable evolutionary programming for optimisation of mechanical design. International Journal of Engineering Intelligent Systems, 7,(2), (1999) 77–82
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Cao, Y.J., Ireson, N., Bull, L., Miles, R. (2000). Distributed Learning Control of Traffic Signals. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_12
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DOI: https://doi.org/10.1007/3-540-45561-2_12
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