Lane-level Traffic Flow Tracing Method Based on Traffic Shockwave Features
To support traffic flow tracing analysis under low-penetration trajectory data in urban roadways,this paper proposes a lane-level traffic flow source tracing method based on traffic shockwave features.Using real vehicle trajectory data from the Next Generation Simulation(NGSIM)dataset,the differences in traffic shockwave features from different origins of traffic flow are analyzed.Combined with signal timing schemes,the feasibility of using traffic shockwaves for flow source analysis is validated across multiple dimensions,including initial vehicle stopping time,the spatiotemporal location of shockwave initiation,slope,and coverage length.Based on this analysis,five shockwave features are extracted to develop four machine learning-based real-time lane-level traffic flow tracing methods.These models are trained and calibrated using the NGSIM data,and the sensitivity to different normalization methods,data volume,and data accuracy is analyzed.The results show that in low data volume scenarios,the features should be normalized using the Min-Max method,with a maximum average percentage error not exceeding 23.60％.When data volume is more abundant(exceeding 100 signal cycles),the Z-Score normalization method is preferred,with the maximum average percentage error not exceeding 9.90％.The gradient-boosting regression model performs best with an average error as low as 0.01％.In addition,the effect of data errors varies from model to model,but the models do not have failure problems when the errors are large.This method is independent of fixed detector data.In the future,the study can be extended to the network-level flow tracing based on traffic shockwave features.
intelligent transportationspatiotemporal tracing of vehiclesmachine learningvehicle trajectoriestraffic shockwave
万方数据
维普
