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A Versatile MultiAgent Traffic Simulator Framework Based on Real Data

Abstract : Among real-system applications of AI, the field of traffic simulation makes use of a wide range of techniques and algorithms. Especially, microscopic models of road traffic have been expanding for several years. Indeed, Multi-Agent Systems provide the capability of modeling the very diversity of individual behaviors. Several professional tools provide comprehensive sets of ready-made, accurate behaviors for several kinds of vehicles. The price in such tools is the difficulty to modify the nature of programmed behaviors, and the specialization in a single purpose, e.g. either studying resulting flows, or providing an immersive virtual reality environment. Thus, we advocate for a more flexible approach for the design of multi-purpose tools for decision support. Especially, the use of geographical open databases offers the opportunity to design agent-based traffic simulators which can be continuously informed of changes in traffic conditions. Our proposal also makes decision support systems able to integrate environmental and behavioral modifications in a linear fashion, and to compare various scenarios built from different hypotheses in terms of actors, behaviors, environment and flows. We also describe here the prototype tool that has been implemented according to our design principles.
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Contributor : Cristal Equipe Smac <>
Submitted on : Thursday, February 25, 2016 - 9:54:44 AM
Last modification on : Friday, December 11, 2020 - 6:44:03 PM



Alexandre Bonhomme, Philippe Mathieu, Sébastien Picault. A Versatile MultiAgent Traffic Simulator Framework Based on Real Data. International Journal on Artificial Intelligence Tools (IJAIT), 2016, Special Issue on 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2014), 25 (1), pp.20. ⟨10.1142/S021821301660006X⟩. ⟨hal-01278889⟩



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