Identify sequence of events likely to result in severe crash outcomes. (November 2016)

• Record Type: Journal Article
• Authors: Wu, Kun-Feng; Thor, Craig P.; Ardiansyah, Muhammad Nashir
• Is Part Of: Accident analysis and prevention. Volume 96(2016)
• Page Start: 198
• Page End: 207
• Publication Date: 2016-11
• Subjects: Crash sequence -- Crash characterization -- FARS -- Crash severity; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2016.08.009
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: This research seeks to explore the use of event sequence to characterize crashes. Crash sequences were grouped using the Optimal Matching approach. This research identifies crash sequences likely to result in severe crash outcomes. Cross median/centerline ROR crashes often result in high crash severity. Occupants who are directly impacted are more likely to die in a ROR crash. Abstract: The current practice of crash characterization in highway engineering reduces multiple dimensions of crash contributing factors and their relative sequential connections, crash sequences, into broad definitions, resulting in crash categories such as head-on, sideswipe, rear-end, angle, and fixed-object. As a result, crashes that are classified in the same category may contain many different crash sequences. This makes it difficult to develop effective countermeasures because these crash categorizations are based on the outcomes rather than the preceding events. Consequently, the efficacy of a countermeasure designed for a specific type of crash may not be appropriate due to different pre-crash sequences. This research seeks to explore the use of event sequence to characterize crashes. Additionally, this research seeks to identify crash sequences that are likely to result in severe crash outcomes so that researchers can develop effective countermeasures to reduce severe crashes. This study utilizes the sequence of events from roadway departure crashes in the Fatality Analysis Reporting System (FARS), and converts the information to form a new categorization called "crash sequences." The similarity distance between each pair of crash sequences were calculated using the Optimal Matching approach. Cluster analysis was applied to group crash sequences that are etiologically similar in terms of the similarity distance. A hybrid model was constructed to mitigate the potential sample selection bias of FARS data, which is biased toward more severe crashes. The major findings include: (1) in terms of a roadway departure crash, the crash sequences that are most likely to result in high crash severity include a vehicle that first crosses the median or centerline, runs-off-road on the left, and then collides with a roadside fixed-object; (2) seat-belt and airbag usage reduces the probability of dying in a roadway departure crash by 90%; and (3) occupants who are seated on the side of the vehicle that experience a direct impact are 2.6 times more likely to die in a roadway departure crash than those not seated on the same side of the vehicle where the impact occurs.