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.
Original language | English |
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Article number | 100136 |
Journal | Analytic Methods in Accident Research |
Volume | 28 |
DOIs | |
State | Published - Dec 2020 |
Funding
The data for this study were provided through a collaborative effort between Virginia Tech Transportation Institute, the U.S. Federal Highway Administration (FHWA), and Oak Ridge National Laboratory (ORNL). The timely assistance and guidance of the ORNL team about data elements is highly appreciated. The authors would also like to recognize the contribution of Alexandra Boggs in proof-reading the manuscript. This paper is based upon work supported by the US National Science Foundation under grant No. 1538139. Additional support was provided by the US Department of Transportation through the Collaborative Sciences Center for Road Safety, a consortium led by The University of North Carolina at Chapel Hill in partnership with The University of Tennessee. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors. An earlier version of this paper was presented in a poster session in Transportation Research Board 97th Annual Meeting. The data for this study were provided through a collaborative effort between Virginia Tech Transportation Institute , the U.S. Federal Highway Administration (FHWA), and Oak Ridge National Laboratory (ORNL). The timely assistance and guidance of the ORNL team about data elements is highly appreciated. The authors would also like to recognize the contribution of Alexandra Boggs in proof-reading the manuscript. This paper is based upon work supported by the US National Science Foundation under grant No. 1538139 . Additional support was provided by the US Department of Transportation through the Collaborative Sciences Center for Road Safety , a consortium led by The University of North Carolina at Chapel Hill in partnership with The University of Tennessee. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors. An earlier version of this paper was presented in a poster session in Transportation Research Board 97th Annual Meeting.
Keywords
- Crash severity
- Driving volatility
- Heterogeneity-in-means
- Heterogeneity-in-variances
- Longitudinal and lateral acceleration
- Multinomial logit
- Naturalistic driving
- Random parameters
- Time to collision