Agent-oriented traffic simulation
Transactions of the Society for Computer Simulation International - Special issue: multi-agent systems and simulation
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Multi agent simulation of unorganized traffic
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ECAI '96 Selected papers from the Workshop on Distributed Artificial Intelligence Meets Machine Learning, Learning in Multi-Agent Environments
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AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
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Autonomous Agents and Multi-Agent Systems
Self-adaptation in a network of social drivers: using random boolean networks
Proceedings of the 2011 workshop on Organic computing
Information sharing for smooth traffic in road networks
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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Traffic is an interesting research area for multi-agent systems, as the inter-dependence of actions leads to a high frequency of implicit coordination decisions among agents. The present work investigates the simulation of a market for traffic information. This market is implemented as a traffic centre where some measurements of the traffic conditions are evaluated. Simulated data generates information which is "sold" to drivers. Different levels of data aggregation, at different costs, are available. We simulate drivers buying this information and evaluating their utility. Based on their perception of this worthiness they will continue with their strategy of buying (or not) particular forms of information, or will abandon or change such a strategy. Our results are twofold. First, they corroborate previous studies from traffic engineers and traffic economists which i) question the presumption that information is necessarily beneficial for traffic as a whole, and ii) state that drivers who rely only on conventional information are likely to have an inaccurate knowledge of traffic conditions. Second, we show that not all types of simple information bring the same payoff to the informed drivers.