Risk Measurement Model for Vehicle Group Based on Temporal and Spatial Similarities

Abstract

Vehicle rear-end collisions are primarily caused by tight car following in a continuous traffic flow, as well as a driver’s incorrect perception of the traffic environment ahead and delayed response. To facilitate an investigation pertaining to rear-end collision mechanisms and accurately measure the risk, the concept of a vehicle group is introduced herein. A risk measurement model for a vehicle group (RMVG) based on temporal and spatial similarities is proposed. First, vehicles are categorized based on their temporal and spatial similarities. Risk measurement metrics are defined based on the traffic composition, movement state, and conflict extent. Subsequently, vehicle group risk identification and risk measurement models based on an isolation forest are established. the rear-end collision risk of the vehicle groups is analyzed both qualitatively and quantitatively. Finally, the RMVG is tested using the vehicle trajectory data set of Longpan South Road, Nanjing City, Jiangsu Province, China, and the results are compared with those of a support vector machine and local outlier factor. The results show that the accuracy of the RMVG is higher than those of other models: its accuracy rate and specificity are 95.68% and 88.89%, respectively, whereas its false alarm rate is only 3.47%.