Abstract
This paper presents a Traffic Lights control system, inspired by Swarm intelligence methodologies, in which every intersection controller makes independent decisions to pursue common goals and is able to improve the global traffic performance. The solution is low cost and widely applicable to different urban scenarios. This work is developed within the COLOMBO european project. Control methods are divided into macroscopic and microscopic control levels: the former reacts to macroscopic key figures such as mean congestion length and mean traffic density and acts on the choice of the signal program or the development of the frame signal program; the latter includes changes at short notice based on changes in the traffic flow: they include methods for signal program adaptation and development. The developed system has been widely tested on synthetic benchmarks with promising results.
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References
Chen, Y., Richard Yu, F., Zhou, B.: Improving throughput in highway transportation systems by entry control and virtual queue. In: Proceedings of the Third ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2013, pp. 9–14. ACM, New York (2013)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from natural to artificial systems, vol. 1. Oxford University Press (1999)
Ducatelle, F., Di Caro, G.A., Gambardella, L.M.: Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intelligence 4(3), 173–198 (2010)
Ferrante, E., Sun, W., Turgut, A.E., Dorigo, M., Birattari, M., Wenseleers, T.: Self-organized flocking with conflicting goal directions. In: Proceedings of the European Conference on Complex Systems 2012, pp. 607–613. Springer (2013)
Slager, G., Milano, M.: Urban traffic control system using self-organization. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 255–260 (2010)
Gershenson, C.: Self-organizing traffic lights (2004). arXiv preprint nlin/0411066
de Oliveira, D., Ferreira Jr, P.R., Bazzan, A.L.C., Klügl, F.: A swarm-based approach for selection of signal plans in Urban scenarios. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 416–417. Springer, Heidelberg (2004)
Seredynski, M., Arnould, G., Khadraoui, D.: The emerging applications of intelligent vehicular networks for traffic efficiency. In: Proceedings of the Third ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2013, pp. 101–108. ACM, New York (2013)
Robertson, D.I.: Transyt: a traffic network study tool (1969)
Robertson, D.I., David Bretherton, R.: Optimizing networks of traffic signals in real time: The scoot method. IEEE Transactions on Vehicular Technology 40(1) (1991)
Sims, A.G.: The sydney coordinated adaptive traffic system. In: Engineering Foundation Conference on Research Directions in Computer Control of Urban Traffic Systems, 1979, Pacific Grove, California, USA (1979)
Peek Traffic. Utopia/spot-technical reference manual. Peek Traffic, Amersfoort,The Netherlands, Tech. Rep (2002)
Priemer, C., Friedrich, B.: A decentralized adaptive traffic signal control using v2i communication data. In: 12th International IEEE Conference on Intelligent Transportation Systems, ITSC 2009, pp. 1–6. IEEE (2009)
Mizuno, K., Fukui, Y., Nishihara, S.: Urban traffic signal control based on distributed constraint satisfaction. In: Hawaii International Conference on System Sciences, Proceedings of the 41st Annual, pp. 65–65. IEEE (2008)
Bazzan, A.L.C.: Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Autonomous Agents and Multi-Agent Systems 18(3), 342–375 (2009)
Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(2), 156–172 (2008)
Greenshields, B.D., Channing, W.S., Miller, H.H., et al: A study of traffic capacity. In: Highway research board proceedings, vol. 1935. National Research Council (USA). Highway Research Board (1935)
Theraulaz, G., Bonabeau, E., Denuebourg, J.N.: Response threshold reinforcements and division of labour in insect societies. Proceedings of the Royal Society of London. Series B: Biological Sciences 265(1393), 327–332 (1998)
Shafiee, K., Lee, J.B., Leung, V.C.M., Chow, G.: Modeling and simulation of vehicular networks. In: Proceedings of the First ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2011, pp. 77–86. ACM, New York (2011)
Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO - Simulation of Urban MObility. International Journal On Advances in Systems and Measurements 5(3&4), 128–138 (2012)
FGSV Verlag Forschungsgesellschaft fuer Strassen-und Verkehrswesen. RiLSA - Richtlinien fur Lichtsignalanlagen - Lichtzeichenanlagen fur den Strassenverkehr. FGSV (1999)
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Caselli, F., Bonfietti, A., Milano, M. (2015). Swarm-Based Controller for Traffic Lights Management. In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds) AI*IA 2015 Advances in Artificial Intelligence. AI*IA 2015. Lecture Notes in Computer Science(), vol 9336. Springer, Cham. https://doi.org/10.1007/978-3-319-24309-2_2
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DOI: https://doi.org/10.1007/978-3-319-24309-2_2
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