摘要：To reveal the impact mechanism of low-speed vehicles (LSVs) on expressway traffic safety, this paper uses the polynomial fitting method to establish evolution models of traffic density and average speed at different LSV speeds in order to explore the queuing and dissipation characteristics of vehicles affected by LSVs and investigate the impact range of LSVs on expressways. Based on the findings above, this paper builds a Surrogate Safety Assessment Model (SSAM)-based model to quantify driving safety and further explore the differences in vehicle conflicts when an LSV moves in different lanes at the same speed. The simulation experiment is conducted based on the field data from the Inner Ring North Road located along the Nanjing Inner Ring High Speed Road. The results show that the evolutionary features of lane traffic density and average speed under different LSV speeds satisfy the octuple polynomial law, reflecting the spatial heterogeneity of vehicle distribution at different LSV driving speeds. Meanwhile, LSVs with different speeds produced the most significant negative impact on the roadway within 400 m of the expressway entrance. The lower the speed of the LSV, the more significant the adverse effect. In addition, this paper finds that when an LSV travels in different lanes at the same speed, the inner, middle, and outer lanes have the highest number of total conflicts, rear-end conflicts, and lane-change conflicts, respectively. Meanwhile, vehicles in the outer lane are the most significantly affected by LSVs, while vehicles in the middle lane are the least affected with the highest traffic efficiency. Additionally, the Maximum Speed (MaxS) and Difference in Vehicle Speed (DeltaS) for the middle lane are 47.9% and 60.5% higher than the outer lane, respectively. Nevertheless, based on the Probability of Unsuccessful Evasive Actions, i.e., P(UEA), vehicles in the middle lane have the highest probability of potential traffic conflicts. The methods used in this paper will have positive implications for establishing autonomous vehicle risk avoidance systems which can improve the safety levels of expressways.
