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Title: How Driving Volatility in Time to Collision Relates to Crash Severity in a Naturalistic Driving Environment

Abstract

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 crashmore » 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.« less

Authors:
 [1];  [1]; ORCiD logo [2]
  1. The University of Tennessee, Knoxville
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1468220
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 2018 Annual Meeting of the Transportation Research Board - Washington, DC, District of Columbia, United States of America - 1/7/2018 5:00:00 AM-1/11/2018 5:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Wali, Behram, Khattak, Asad, and Karnowski, Thomas Paul. How Driving Volatility in Time to Collision Relates to Crash Severity in a Naturalistic Driving Environment. United States: N. p., 2018. Web.
Wali, Behram, Khattak, Asad, & Karnowski, Thomas Paul. How Driving Volatility in Time to Collision Relates to Crash Severity in a Naturalistic Driving Environment. United States.
Wali, Behram, Khattak, Asad, and Karnowski, Thomas Paul. Mon . "How Driving Volatility in Time to Collision Relates to Crash Severity in a Naturalistic Driving Environment". United States. https://www.osti.gov/servlets/purl/1468220.
@article{osti_1468220,
title = {How Driving Volatility in Time to Collision Relates to Crash Severity in a Naturalistic Driving Environment},
author = {Wali, Behram and Khattak, Asad and Karnowski, Thomas Paul},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
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