Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

Hui Zhang,Chaozhong Wu,Qi Zhang,Yafen Wang,Siyao Li

doi:10.1155/2020/8523818

Hui Zhang, Chaozhong Wu + Show 3 more

Open Access

https://doi.org/10.1155/2020/8523818

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Abstract

Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway.

Highlights

With the rapid development of Chinese cities, residents’ demand for travel and the increasing number of motor vehicles put forward higher requirements for the operation efficiency of cities
All the traffic variables along with their multiplicative interaction combinations and rain were taken as explanatory variables in both multivariate models. e best fitted variable combination for crash frequency prediction included all traffic and weather variables plus the interaction between average speed and speed limit
E result shows that the negative binomial model has good prediction performance for the rear-end collision and the side-impact collision. e significant variables in the two models include average speed, traffic volume, weather, speed limit, and the interaction between average speed and speed limit

Summary

Introduction

With the rapid development of Chinese cities, residents’ demand for travel and the increasing number of motor vehicles put forward higher requirements for the operation efficiency of cities. Compared with main arterial roads, urban expressway is characterized by large traffic volume and higher speed [1], resulting in frequent traffic crashes in recent years. E Highway Safety Manual (HSM) prediction model represents the most widely used approach for road safety assessment, developing the safety prediction procedures for rural highways, urban and suburban arterials in the 2010 version of the HSM [7]. Taking factors such as traffic flow, road geometry, and so on into consideration, the HSM provides a prediction method for estimating the expected average frequency of single- and multiple-vehicle fatal-and-injury crashes

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