Exploring the combined effects of driving situations on freeway rear-end crash risk using naturalistic driving study data

Kun Feng (Ken) Wu*, Lan Wang

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract

    The causes and the crash-generating processes of freeway rear-end (FRE) crashes are complicated. Previous studies have highlighted the many contributing factors to crash occurrences on freeways, such as traffic flow conditions, driver-following behavior, driver attention allocation, driver characteristics, the driving environment, and drivers’ interactions with surrounding vehicles, etc. Nevertheless, few studies have looked into the combined effects of these factors on FRE crash risk as a whole. This study focuses on characterizing the sequential crash generating process of the interactions between traffic flow conditions, roadway attributes, driver behavior, event attributes, and precipitating events in FRE crashes. A sequential modeling framework for modeling the sequential and combined effects on FRE crash risk was constructed by applying structural equation modeling (SEM). The Second Highway Strategic Research Program (SHRP2) Naturalistic Driving Study (NDS) data was utilized for this purpose as this data provides extensive information concerning what happened before crashes and near-crashes. A total of 17 and 433 FRE crashes and near-crashes, respectively, were included in this study. It was found that (1) FRE crashes were associated with the sequential and combined effects of those factors above; (2) certain types of speed oscillations were identified as precursors to sudden braking when vehicles ahead decelerated or stopped-and-went; and (3) many factors were identified as being associated with driver perception time and crash occurrence.

    Original languageAmerican English
    Article number105866
    JournalAccident Analysis and Prevention
    Volume150
    DOIs
    StatePublished - Feb 2021

    Keywords

    • NDS
    • Rear-end crash
    • SEM
    • SHRP2

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