Traffic congestion estimation on urban road segments is crucial to traffic management. Considering the heterogeneous impact of dynamic critical bottleneck (e.g., arterials) on congestion diffusion, this study proposes a traffic congestion estimation method by using GPS trajectory data. At first, the process of congestion diffusion is modeled by percolation theory, and the critical threshold qcT in time interval T is inferred to represent the network-wide traffic states. Then, qcT is utilized as the baseline to characterize the heterogeneous impact of dynamic critical bottlenecks on congestion diffusion. Finally, the Systemic Congestion Index (SCI) is generated to estimate segment-based congestion intensity. Investigations revealed that compared with the speed, relative velocity (RV), Travel Time Index (TTI), and the ground truth data (i.e. occupancy), the proposed method can capture the spatial-temporal variation of congestion. Moreover, the reliability of SCI is verified by the Dynamic Time Warping algorithm (DTW) and a sensitivity analysis.
Xu et al. (Tue,) studied this question.