How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles

Yuntao Shi,Ye Li,Hao Zhang,Dan Wu,Qing Cai

doi:10.3390/su12218941

Abstract

Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.

Highlights

Traffic crashes cause grave losses of life and property all over the world
Two surrogate safety measures derived from the time-to-collision (TTC) index were applied for longitudinal safety evaluations, including the time exposed time-to-collision (TET) and time integrated time-to collision (TIT)
Random1 refers to the following simulation experiments, where we investigate individual heterogeneity by randomly choosing parameter groups from the 513 groups, and all five parameters of each group are selected as a whole

Summary

Introduction

The rear-end crash is the most frequently occurring type, resulting in approximately 34% of the total reported crashes in the United States [1]. Considerable studies have been conducted to reduce rear-end crashes [3,4,5,6]. Most efforts have been taken to develop a variety of crash prediction models to investigate contributing factors of rear-end crashes. Reported historical crash data are exploited and various factors, such as traffic volume, speed, weather, vehicle type and others, are examined in various models [7,8,9,10]. There are some non-parametric methods based on machine learning models proposed for rear-end crash analysis [11,12]

Methods

Results

Conclusion