01657927

Component

The sequence of instantaneous driving decisions and its variations, known as driving volatility, can be a leading indicator of unsafe driving practices. The research issue is to characterize volatility in instantaneous driving decisions in longitudinal and lateral direction and to seek an understanding of how driving volatility relates to crash severity. By using a unique real-world naturalistic driving database from the SHRP 2, 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. 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 ordered models with heterogeneity in parameter means are estimated. The findings suggest that greater driving volatility (both in longitudinal and lateral direction) prior to crash occurrence increases the likelihood of police reportable or 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 significantly outperformed both the fixed parameter and random parameter counterparts. The relevance of the findings to the development of proactive behavioral countermeasures for drivers is discussed.

This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation. This is an alternate title: How Driving Volatility in Time to Collision Relates to Crash Severity in a Naturalistic Driving Environment.

18-00060

English

0000-0001-9969-7503

7p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Automatic data collection systems; Crash severity; Driver performance; Driving; Highway safety; Traffic crashes

Time-to-collision

Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2018 Paper #18-00060

TRIS, TRB, ATRI

Jan 8 2018 10:02AM