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McCluskey, T.L. (2011)
Publisher: he Society for the Study of Artificial Intelligence and Simulation of Behaviour
Languages: English
Types: Article
Subjects: QA75
Mobility of people and goods is a key challenge for the future. Transport is one of the world's largest industrial sectors, yet challenges and frequent failures of road transportation networks are well known, with the cost of congestion alone estimated at Euro 100 billion in the EU[1]. \ud Systems of road traffic flow are affected by the outcome of individual driving decisions, often assisted by personalised navigation and information-providing devices. This combined with the complex topology of the network and the random occurrence of capacity reducing events make for a complex system. Within this system control centres utilise a range of assets (traffic signal, variable speed limits, re-routing etc) to help optimise the flow of network traffic with respect to a range of rules, regulations and policies relating to efficiency, safety and environmental criteria.
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    • [1] European Commission. Towards a new culture for urban mobility. COM(2007) 551 final. Brussels, 25.9.2007. [2] J. C. Miles and A. J. Walker. Science Review: The potential application of artificial intelligence in transport. Technical report, Foresight Intelligent Infrastructure Systems Project, 2007.
    • [3] Kephart, J. O. and Chess, D. M. (2003) The vision of autonomic computing. Computer 36(1).
    • [4] Balbo, F., Pinson, S., 2010. “Using intelligent agents for Transportation Regulation Support System design”, Transportation Research Part C: Emerging Technologies, Volume 18 (1), 140-156.
    • [5] Dusparic, I., and Cahill, V. (2009 ). “Distributed W-Learning: Multi-policy optimization in selforganizing systems” in Proceedings of the 3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems ( SASO 2009), pages 20-29, IEEE, 2009.
    • [6] Bazzan, A. L. (2005). A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multi-Agent Systems, 10 (1), 131-164.
    • [7] Bhouri N., Balbo F., and Pinson S., A multi-Agent System to Regulate Bimodal Urban Traffic (2010), Proc. European Workshop on Maulti-Agent Systems, EUMAS 2010, Paris, France, December 2010.
    • [8] Branke, Mnif, Müller-Schloer, Prothmann, Richter, Rochner, Schmeck (2006) Organic computing - addressing complexity by controlled self-organization. In Proceedings IEEE ISOLA 2006, Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation.
    • [9] McCluskey, T. L.; West, M.M. (2001) The Automated Refinement of a Requirements Domain Theory. Journal of Automated Software Enginnering, vol 8, pp 195-218, (Special Issue on Inductive Programming), Kluwer Academic Publishers, April 2001.
    • [10] Prothmann H., Branke J., Schmeck H., Tomforde S., Rochner F., Hahner J., and Muller-Schloer C. (2009) Organic traffic light control for urban road networks. International Journal of Autonomous and Adaptive Communications Systems 2(3).
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