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Multi-Agent Planning and Simulation for Intelligent Transportation Systems

In this paper, we provide a brief survey of our recent work on multi-agent planning and simulation for intelligent transportation system. In particular, we first present a novel algorithm to reconstruct and visualize continuous traffic flows from discrete spatio-temporal data provided by traffic sensors. Given the positions of each car at two recorded locations on a highway and the corresponding time instances, our approach can reconstruct the traffic flows (i.e. the dynamic motions of multiple cars over time) in between the two locations along the highway using a priority-based multi-agent planning algorithm. Our algorithm is applicable to high-density traffic on highways with an arbitrary number of lanes and takes into account the geometric, kinematic, and dynamic constraints on the cars. In addition, we describe an efficient method for simulating realistic traffic flows on large-scale road networks. Our technique is based on a continuum PDE model of traffic flow that we extend to correctly handle lane changes and merges, as well as traffic behaviors due to changes in speed limit. We show that our method can simulate plausible traffic flows on publiclyavailable, real-world road data and demonstrate the scalability of this technique on many-core systems.