Traffic shockwaves reflect a transition from the free-flow traffic state to the congested state. They create potentially unsafe situations for drivers, increase travel time, and significantly reduce freeway capacity. Several shockwave detection methods based on loop detector data and other traditional databases have been around for years. However, these methods face certain accuracy and reliability issues, many of which are due to the nature and accuracy of available data. Connected-vehicles technology is expected to provide reliable and accurate data about individual vehicles that can be potentially used for shockwave detection. Accordingly, this paper presents a novel method to identify shockwave formation and track its propagation based on the speed distribution of individual vehicles available through connected-vehicles technology. In addition, this paper analyzes the impact of partial connectivity on shockwave identification and compares the accuracy of the proposed method to a wavelet transformation-based method. Vehicle trajectories from the Next Generation Simulation (NGSIM) US-101 dataset were analyzed. The analysis shows a consistent pattern in which shockwave formation, indicated by a drop in speed propagating over space and time, is associated with a sharp increase in the value of speed standard deviation (SSD). Furthermore, the analysis shows that shockwaves can be accurately identified using vehicle trajectory data from connected vehicles at minimum 30% market penetration rates. Finally, the results show that the SSD of individual vehicles is more responsive to shockwave formation than the mean speed wavelet transformation, which can lead to improved shockwave detection accuracy.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering