A Particle Filter-Based Approach for Vehicle Trajectory Reconstruction Using Sparse Probe Data

Trajectory data collected from probe vehicles become increasingly important for urban traffic operation and management. However, current data tend to be sparse in time and space due to technical constraints or privacy concerns, which fail to provide a complete picture of traffic flow. This study proposes a particle filter (PF) based approach to reconstruct the vehicle trajectory for signalized arterial using sparse probe data. First, the arterial intersection is divided into multiple road cells and the estimation of cell travel time is formulated as a quadratic programming problem. Then, PF is applied to reconstruct the incomplete vehicle trajectory between consecutive updates. Specifically, to calculate and update the weight of initial particles, three measurability criteria are designed for importance sampling considering the structure of signalized arterial and the feature of vehicular updates, i.e., travel time adjustment accuracy, arterial link speed limit and travel time adjustment possibility. Last, NGSIM trajectory data are extracted at intervals to construct the sparse data, which are used to verify the effectiveness of the proposed method. The results show that reconstructed trajectories match closely with ground truth both at the single intersection and along the arterial with multiple intersections.