The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment. (December 2020)

• Record Type: Journal Article
• Authors: Wali, Behram; Khattak, Asad J.; Karnowski, Thomas
• Is Part Of: Analytic methods in accident research. Volume 28(2020)
• Publication Date: 2020-12
• Subjects: Naturalistic driving -- Driving volatility -- Time to collision -- Longitudinal and lateral acceleration -- Crash severity -- Multinomial logit -- Random parameters -- Heterogeneity-in-means -- Heterogeneity-in-variances; Accidents -- Research -- Methodology -- Periodicals; Accidents -- Prevention -- Periodicals; 363.100721
• DOI: 10.1016/j.amar.2020.100136
• Languages: English
• ISSNs: 2213-6657

Abstract

Highlights: Driving volatility in time to collision and crash-injury severity is investigated. Naturalistic crash events with 0.2 million temporal driving samples are analyzed. Systematic and random heterogeneity is accounted. Heterogeneity-in-means and variances random parameter models are developed. Greater driving volatility increases the risk of more severe crashes. Abstract: As a key indicator of unsafe driving, driving volatility characterizes the variations in microscopic driving decisions. This study characterizes volatility in longitudinal and lateral driving decisions and examines the links between driving volatility in time to collision and crash-injury severity. By using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 671 crash events featuring around 0.2 million temporal samples of real-world driving are analyzed. Based on different driving performance measures, 16 different volatility indices are created. To explore the relationships between crash-injury severity outcomes and driving volatility, the volatility indices are then linked with individual crash events including information on crash severity, drivers' pre-crash maneuvers and behaviors, secondary tasks and durations, and other factors. As driving volatility prior to crash involvement can have different components, an in-depth analysis is conducted using the aggregate as well as segmented (based on time to collision) real-world driving data. To account for the issues of observed and unobserved heterogeneity, fixed and random parameter logit models with heterogeneity in parameter means and variances are estimated. The empirical results offer important insights regarding how driving volatility in time to collision relates to crash severity outcomes. Overall, statistically significant positive correlations are found between the aggregate (as well as segmented) volatility measures and crash severity outcomes. The findings suggest that greater driving volatility (both in longitudinal and lateral direction) in time to collision increases the likelihood of police reportable or most severe crash events. Importantly, compared to the effect of volatility in longitudinal acceleration on crash outcomes, the effect of volatility in longitudinal deceleration is significantly greater in magnitude. Methodologically, the random parameter models with heterogeneity-in-means and variances significantly outperformed both the fixed parameter and random parameter counterparts (with homogeneous means and variances), underscoring the importance of accounting for both observed and unobserved heterogeneity. The relevance of the findings to the development of proactive behavioral countermeasures for drivers is discussed.