LAUSR

The identification of a traffic shockwave traditionally is conducted offline, which inhibits its implementation for active traffic management strategies. This study aims to develop an online approach to detect traffic shockwaves on freeways, particularly the end-of-queue shockwaves, using spacing-based probe vehicles (SPVs) that can obtain the trajectories of its leading and/or following vehicles. The proposed framework consists of four stages: (i) local shockwave (LSW) position detection, (ii) LSW speed estimation, (iii) grouping of LSWs into a whole shockwave (WSW) and (iv) WSW speed estimation. In particular, two alternatives, namely the line connection-based method and the Lighthill–Whitham–Richards model-based method (LWRM), are proposed for stage 2, and other two alternatives, namely the simple averaging method and the hybrid method (HM), are proposed for stage 4. A set of next generation simulation data are utilised to evaluate the performance of the proposed method. The results demonstrate that the combination of LWRM + HM outperforms among the four combined methods. A series of the analysis indicate that the proposed method is computationally efficient, accurate and more importantly, it is applicable to sensor data from SPVs with real-world noise.