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Crash Factor Analysis in Intersection-Related Crashes Using SHRP 2 Naturalistic Driving Study Data

Journal Article
2021-01-0872
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 06, 2021 by SAE International in United States
Crash Factor Analysis in Intersection-Related Crashes Using SHRP 2 Naturalistic Driving Study Data
Sector:
Citation: Jia, B., Guenther, D., and Heydinger, G., "Crash Factor Analysis in Intersection-Related Crashes Using SHRP 2 Naturalistic Driving Study Data," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(5):2710-2718, 2021, https://doi.org/10.4271/2021-01-0872.
Language: English

Abstract:

Intersections have a high risk of vehicle-to-vehicle conflicts because of the overlapping traffic flow from multiple roads. To understand the factors contributing to the crashes, this study examines the common characteristics in intersection-related crash and near- crash events, such as the existence of traffic control devices, the driver at fault, and occurrence of visual obstructions. The descriptive data of the crash and near-crash events recorded in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) database is used in categorization and statistical analysis in this study. First, the events are divided into seven categories based on trajectories of the conflicting vehicles. The categorization provides the basis for in-depth analysis of crash-contributing factors in specific confliction patterns. Subsequently, descriptive statistics are used to portray each of the categories. The severity of the categories is determined based of the frequency of occurrences of the crashes and near-crashes. Factors contributing to each crash category are then examined based on their frequency of occurrence with the categories. This study reveals the severity of different intersection-related crash patterns, as well as the effects of some crash-contributing factors. The information presented in this paper has the potential to guide investigators interested in developing simulation and test track scenarios to evaluate vehicles equipped with automated driving systems (ADS).