Abstract In this chapter, we investigate a multiagent based approach to modeling autonomic features in urban traffic management. We provide a conceptual model of a traffic system comprising traffic participants modeled as locally autonomous agents, which act to optimize their operational and tactical decisions (e.g., route choice), and traffic management center(s) (TMC) which influence the traffic system according to dynamically selected policies. In this chapter, we focus on two autonomic features which emerge from the local decisions and actions of traffic participants and their interaction with the TMC and other vehicles: (1) Autonomic routing, in which we study how vehicle agents can individually adapt routing decisions based on local learning capabilities and traffic information communicated truthfully by a traffic management center; and (2) Autonomic grouping, i.e., collective decision-making of vehicles, which exchange route information and dynamically form and operate groups to drive in a convoy, thus aiming at higher speed and increased throughput. Communication is based on a (simulated) vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) protocols. Initial experiments are reported using a real-world traffic scenario modeled in the Aimsun software, which is extended by the decision logic of TMC and vehicles. The experiments indicate that autonomic routing and grouping can improve the performance of a traffic management network, even though negative effects such as unstable behavior can be observed in some cases.


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    Autonomic Road Transport Support Systems: An Introduction

    McCluskey, Thomas Leo / Kotsialos, Apostolos / Müller, Jörg P. et al. | Springer Verlag | 2016





    Test Bed for Multiagent Control Systems in Road Traffic Management

    van Katwijk, R. / van Koningsbruggen, P. / De Schutter, B. et al. | Transportation Research Record | 2005