A large vehicle first clustering method based road section risk level estimation

On-road large vehicles are always considered as potential danger, which could generate serious damage and strongly threaten road safety. Hence, large vehicle related accident forecasting becomes an important research topic in intelligence transportation area. In this paper, a large vehicle first clustering method (LVFC) is proposed to estimate the risk level of vehicle group. Composite performance index (CSPI), which is used to illustrate the risk level of hotspots area, has become a popular practice in the field of road safety. CSPI value is employed to illustrate environment condition of object road section. Combining CSPI with vehicle group's risk level, a comprehensive risk parameter is presented to represent real-time transportation condition, which illustrates the risk level of a road section with large vehicle group passing by. A vehicle following model is designed to simulate the generation of large vehicle first clusters and it's changing procedure, which provides justification for the proposed algorithm.