Method for short-term traffic risk prediction of road sections using roadside observation data

Abstract

Disclosed is a method for short-term traffic risk prediction of road sections by using roadside observation data. The method includes the following steps: 1) vehicle trajectory data in the detection area is obtained by using roadside observation data; 2) according to the continuous driving trajectories in the detection area, the traffic flow indicators are counted, and the surrogate safety indicators between vehicles are calculated; 3) time to collision and deceleration are selected as identification indicators to identify conflict events with collision risk in the detection area; 4) traffic flow indicators and surrogate safety indicators within the set time before the occurrence of conflict events are extracted, and the feature screening of various extracted indicators is performed by using classification algorithms; 5) based on the selected feature indicators, the indicators with the highest importance ranking are selected as the input to build a short-term traffic risk prediction model, and the model training and testing are completed by using the identified conflict events; 6) the short-term traffic risk prediction model is used to predict the risk of road sections. The proposed method can improve the prediction accuracy rate of road sections.