Automatic Traffic Shockwave Identification Using Vehicles’ Trajectories

Knowledge of the location and speed of shockwaves in a traffic stream provides insight into the formation and dissipation of congestion – information which is important for system managers. Furthermore, this information can be used to estimate and predict travel time for a section of a roadway. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This paper proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data. In the proposed methodology first the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified and then a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is used to find propagation speed and spatial and temporal extent of each shockwave. The framework is evaluated using data obtained from a simulation of a signalized intersection and also real trajectory data from freeway US-101 near Los Angeles and shows promising results.