Learning Traffic Flow Dynamics using Random Fields

This paper presents a mesoscopic stochastic model for the reconstruction of vehicle trajectories from data made available by subsets of (probe) vehicles. Long-range vehicle interactions are applied in a totally asymmetric simple exclusion process to capture information made available to connected and autonomous vehicles. The dynamics are represented by a factor graph, which enables learning of traffic dynamics from historical data using Bayesian belief propagation. Adequate probe penetration levels for faithful reconstruction on single-lane roads is investigated. The estimation technique is tested using a vehicle trajectory dataset generated using an independent microscopic traffic simulator. Although the parameters of the traffic state estimation model are learned from (simulated) historical data, the proposed algorithm is found to be robust to unpredictable conditions. Moreover, by exposing the algorithm to varying traffic conditions with increasingly larger datasets, the probe penetration rates required to capture the traffic dynamics effectively can be substantially reduced. The results also highlight the need to take into account randomness in the spatio-temporal coverage associated with probe data for reliable state estimation algorithms.