Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS

Bo Yang,Jie Bao,Weihua Zhang,Yao Wu

doi:10.3390/su12187386

Abstract

In this study, collision-related data were collected on the I-880 freeway of California in the United States from 2006 to 2011. Our objective was to study the collision probability of different collision types and severities in different traffic states. The traffic states were divided by the traditional level of service (LOS) method. Various Bayesian conditional logit models have been established to analyze the relationship between the collision probability of different collision patterns and LOSs. The results showed that LOS A had the best safety performance associated with all of the collision types and severities, LOS C had the worst safety performance associated with hit object collisions, LOS D had the worst safety performance associated with sideswipe collisions and rear end collisions, and LOS F had the worst safety performance associated with injury collisions. The five-stage Bayesian random parameter sequential logit model was established to quantify the effects of different variables on the collision probability of various collision types and severities. In addition to LOS, the visibility, road surface, weather, ramp, and number of lanes had significant effects on different collision types and severities.

Highlights

With the widespread use of freeway traffic surveillance systems, researchers have started using high-resolution dynamic traffic flow data to identify traffic conditions before collision occurrences
The main purpose was to identify the relationship between level of service (LOS) and different collision types and severities, and explore how contributing factors affect collision risks for different types and severities
The collision-related data were obtained from the I-880 freeway, which is located in California, United States

Summary

Introduction

With the widespread use of freeway traffic surveillance systems, researchers have started using high-resolution dynamic traffic flow data to identify traffic conditions before collision occurrences. Numerous studies have developed real-time collision probability models for estimating the relative probability of collisions, given dynamic traffic flow data [1,2,3,4,5]. These studies have generally used a case-controlled study design structure, in which the traffic conditions before collisions were considered collision cases, while those under collision-free conditions were considered control cases. With the case-controlled dataset, researchers have developed real-time collision probability models to analyze the relationship between the probability of a collision and the traffic-related variables, including geometric design factors, environment factors, traffic flow factors, crash characteristic factors, driver behavior factors, and control strategy factors on a freeway. It has been proven that there is a significant difference in safety performance for different collision types and severities in various traffic flow states [6,7,8]

Objectives

Methods

Results

Conclusion