Two-Stage Detection of Incident-Induced Congestion at the Cycle and Movement Levels on Signalized Urban Roads Using Spatially Sparse Trajectory Data

Accurate and timely detection of incident-induced congestion (IIC) is essential for mitigating its negative impact on traffic efficiency. Existing studies on IIC detection mainly focus on traffic flow on freeways and face challenges on urban roads due to the impacts of signal lights at intersections and diverse road networks. Additionally, the low penetration rate of probe vehicle trajectories poses another challenge. This study proposes a probe-vehicle-trajectory-based algorithm for IIC detection on urban roads at the movement and cycle levels. Two critical features (i.e., the average speed and the entrance time into the road segment) are defined to capture the characteristics of trajectory segments. A Vehicle Trajectory Polar Coordinate Transformation (VTPCT) method is proposed to differentiate anomalous trajectory segments (ATS) affected by IIC from normal ones, considering the periodicity of fixed signal timing at the intersections. Anomaly rates calculated from the identified ATS within a spatiotemporal window are introduced to reflect the movement-cycle-level traffic states. A two-stage algorithm framework is designed to enhance the algorithm’s adaptability to spatially sparse trajectories and diverse road networks. Experimental studies show that the proposed algorithm is applicable to trajectory data with low penetration rates and outperforms benchmarks of typical statistical and AI-based algorithms.