A data-driven traffic shockwave speed detection approach based on vehicle trajectories data

AbstractTraffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves’ turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow.Keywords: clusteringconnected vehiclemachine learningshockwavesmoothening AcknowledgmentsThis research is partially supported by the National Science Foundation awards CMMI-1901994, CMMI-2213459 and CNS-2124858. The authors would like to extend their gratitude to the reviewers and editor for their insightful comments, which have increased the quality of this paper.Authors’ contributionsThe authors confirm their contribution to the paper as follows: Dr. L. Du initiated this idea and supervised the whole study. Students K. Yang and Dr. H. Yang conducted the approach development, implementation, and data collection. All three authors drafted, edited, and reviewed the manuscript. They all reviewed the results and approved the final version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 This threshold is set offline based the traffic data in our experiments. Our approach is not very sensitive to this threshold. It can some values around 10 mph based on how you define the slow traffic in the applications.Additional informationFundingThis research is partially supported by the National Science Foundation awards CMMI-1901994, CMMI-2213459 and CNS-2124858. The authors would like to extend their gratitude to the reviewers and editor for their insightful comments, which have increased the quality of this paper.