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How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from
Ramin Arvin1, Mohsen Kamrani1, Asad J Khattak1
1The University of Tennessee, Knoxville, United States.
Accident; Analysis and Prevention
|March 10, 2019
Summary
Driving volatility, a new metric for vehicle movement, helps identify intersection crash risks. High longitudinal volatility correlates with rear-end crashes, while lateral volatility impacts sideswipe, angle, and head-on collisions.
Area of Science:
- Transportation Engineering
- Traffic Safety Research
- Data Science in Mobility
Background:
- Connected and automated vehicles generate big data for enhanced transportation safety metrics.
- Driving volatility quantifies instantaneous driving behavior variations using vehicular movement data.
Purpose of the Study:
- To develop a methodology for quantifying vehicular movement variations using longitudinal and lateral volatilities.
- To proactively study the impact of instantaneous driving behavior on intersection crash types.
Main Methods:
- Analyzed over 125 million Basic Safety Message data from 2800+ connected vehicles.
- Integrated vehicular data with historical crash and road inventory data for 167 intersections.
- Quantified 30 measures of driving volatility using speed, acceleration, and yaw-rate, analyzed with statistical models.
Main Results:
- Longitudinal volatility is highly correlated with rear-end crashes at intersections.
- Lateral volatility, along with speed and longitudinal volatility, correlates with sideswipe and angle crashes.
- Speed, longitudinal, and lateral acceleration volatilities are associated with head-on crashes.
Conclusions:
- Driving volatility measures can proactively identify high-risk intersections.
- Understanding driving volatility can inform countermeasures to reduce crash risk.
- Methodology offers insights into driver behavior and its impact on road safety.
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