Abstract
With the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. Variation of traffic flow is considered to have significant impact on the safety performance of expressways. Therefore, the motivation of this study is to explore the mechanism of how the variation of traffic flow measurements such as average speed, speed variation and traffic volume impact the crash risk. Firstly, the crashes were classified according to crash type and vehicles involved: and they are labeled with rear-end collisions or side-impact collisions, they are labeled with heavy-vehicle related collisions or light-vehicle related collisions as well. Then, the corresponding crash data were aggregated based on the similarity of traffic flow conditions and types of crashes. Finally, a random effect negative binomial model was introduced to consider the heterogeneity of the crash risk due to the variance within the traffic flow and crash types. The results show that the significant influencing factors of each type of crashes are not consistent. Specifically, the percentage of heavy vehicles within traffic flow is found to have a negative impact on rear-end collisions and light-vehicle-related collisions, but it has no obvious correlation with side-impact collisions and heavy-vehicle-related collisions. Average speed, speed variation and traffic volume have an interactive effect on the crash rate. In conclusion, if the traffic flow is with higher speed variation within lanes and is with lower average speed, the risk of all types of crashes tends to be higher. If the speed variation within lanes decreases and the average speed increases, the crash risk will also increase. In addition, if the traffic flow is under the conditions of higher speed variation between lanes and lower traffic volume, the risk of rear-end collisions, side-impact collisions and heavy-vehicles related collisions tend to be higher. Meanwhile, if the speed variation between lanes decreases and the traffic volume increases, the crash risk is found to increase as well.
Highlights
Publisher’s Note: MDPI stays neutralWith the fast rapid development and improvement of traffic detection and information communication technology, collecting massive amounts of and high-precision real-time traffic flow and crash data is becoming much easier
Studies show a positive relationship between speed and speed variation with crash rates [4,7], as the research results of Wang’s study [4] show that if the average speed of urban arterials increased by 1%, the crash frequency will increase by 0.7%, and the crash frequency will with regard to jurisdictional claims in published maps and institutional affiliations
The shows the changes in the speed variation among lanes and the within-lane speed variation traffic volume and larger speed variation at those times may be the reason for such an over time
Summary
With the fast rapid development and improvement of traffic detection and information communication technology, collecting massive amounts of and high-precision real-time traffic flow and crash data is becoming much easier. A similar conclusion was found by Theofilatos’s finding that traffic variations were found to significantly influence accident likelihood on urban arterials [15] It can be concluded from the above studies indicate that developing refined models based on crash types and road types can help to better understand the mechanisms of crashes [16,17]. The segment-based method has been widely used in crash frequency prediction research such as in the “Highway Safety Manual” [18] This method studies the relationship between crash frequency and average traffic conditions represented by annual average daily traffic (AADT). In order to address the heterogeneity issues of traffic variations, a random effect negative binomial model is introduced to study the relationship between traffic variation and crash frequency on urban expressways in this paper. It is believed that the results from this paper should be able to provide theoretical support for real-time early warning of road safety, for urban expressways
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