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Traffic Simulation, Reconstruction, and Route Planning
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Interactive Hybrid Simulation of Large-scale TrafficJason Sewall, David Wilkie, and Ming C. Lin We present a novel, real-time algorithm for modeling large-scale, realistic traffic using a hybrid model of both continuum and agent-based methods for traffic simulation. We simulate individual vehicles in regions of interest using state-of-the-art agent-based models of driver behavior and use a faster continuum model of traffic flow in the remainder of the road network. Our key contributions are efficient techniques for the dynamic coupling of discrete vehicle simulation with aggregate behavior of continuum traffic. We demonstrate the flexibility and scalability of our interactive visual simulation techniques on extensive road networks using both real-world traffic data and synthetic scenarios. |
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Self-aware Traffic Route PlanningDavid Wilkie, Jur van den Berg, Ming C. Lin, and Dinesh Manocha One of the most ubiquitous artificial intelligence (AI) applications is vehicle route planning. While state-of-the-art systems take into account current traffic conditions or historic traffic data, current planning approaches ignore the impact of their own plans on the future traffic conditions. We present a novel algorithm for self-aware route planning that uses the routes it plans for current vehicle traffic to more accurately predict future traffic conditions for subsequent cars. Our planner uses a roadmap with stochastic, time-varying traffic densities that are defined by a combination of historical data and the densities predicted by the planned routes for the cars ahead of the current traffic. We have applied our algorithm to large-scale traffic route planning, and demonstrated that our self-aware route planner can more accurately predict future traffic conditions, which results in a reduction of the travel time for those vehicles that use our algorithm. |
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Continuum Traffic SimulationJason Sewall, David Wilkie, Paul Merrell, and Ming C. Lin We present a novel method for the synthesis and animation of realistic traffic flows on large-scale road networks. Our technique is based on a continuum model of traffic flow we extend to correctly handle lane changes and merges, as well as traffic behaviors due to changes in speed limit. We demonstrate how our method can be applied to the animation of many vehicles in a large-scale traffic network at interactive rates and show that our method can simulate believable traffic flows on publicly-available, real-world road data. We furthermore demonstrate the scalability of this technique on many-core systems. |
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Virtualized TrafficJur van den Berg, Jason Sewall, Ming C. Lin, and Dinesh Manocha We present the concept of virtualized traffic to reconstruct and visualize continuous traffic flows from discrete spatial and temporal data provided by traffic sensors or generated artificially to enhance a sense of immersion in a dynamic virtual world. Our approach can reconstruct the traffic flows in between the two locations along the highway for immersive visualization of virtual cities or other environments. Virtualized traffic 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. |
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