This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific speed-spacing relations, i.e., parametric uncertainty. It also results in smooth vehicle trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby, overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. We utilize ensemble filtering techniques for data assimilation (traffic state estimation), but derive the mean and covariance dynamics as the ensemble sizes go to infinity, thereby bypassing the need to sample from the parameter distributions while estimating the traffic states. As a result, the estimation algorithm is just a standard Kalman-Bucy algorithm, which renders the proposed approach amenable to real-time applications using recursive data. Data assimilation examples are performed and our results indicate good agreement with out-of-sample data.
Centralized adaptive traffic control strategy design across multiple intersections based on vehicle path flows: An approximated Lagrangian decomposition approach
Department of Civil and Environmental Engineering, Mississippi State University（密西西比州立大学土木与环境工程系）
A Novel Approach to Real-Time Short-Term Traffic Prediction based on Distributed Fiber-Optic Sensing and Data Assimilation with a Stochastic Cell-Automata Model
NEC Corporation（日本电气株式会社）；Central Nippon Expressway Company Limited（中日本高速道路株式会社）
On the Robotic Uncertainty of Fully Autonomous Traffic: From Stochastic Car-Following to Mobility-Safety Trade-Offs
organization= Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science；organization= Department of Civil \& Environmental Engineering, University of Wisconsin-Madison , city= Madison , postcode= 53706 , state= Wisconsin , country= United States
