Extracting driving volatility from connected vehicle data in exploring Space-Time relationships with crashes in the city of Saint Louis

Abstract

The analysis of factors that influence the occurrence of roadway crashes within a specified locality have historically been reliant on the assessment of physical infrastructure, historical crash frequency, environmental factors and driver characteristics. The consensus over the years has been drawn to the idea that human factors, specifically regarding driving behaviors, account for the majority of crash outcomes on our roadways. With the emergence of connected vehicle data in the last few years, the capacity to analyze real time driving behavior has become a possibility for safety analysts. Driving volatility has emerged as a valuable proxy for driving behavior and indicator of safety. In this study, evidence of the spatial relationship between driving volatility and historical crash hotspots is uncovered. Utilizing an entropy-based analysis, this study discovered generally strong positive spatial relationships between locations of volatile driving events and historical crashes, with R2 values ranging from 0.015 to 0.970 and a mean of 0.612 for hard accelerations, and 0.048 to 0.996 and a mean of 0.678 for hard decelerations. Including temporal context presented insights showing that the relationships are significant for over 60 % of the coverage area usually between the hours of 7 am to 7 pm, with average R2 values of 0.594 for hard accelerations, and 0.629 for hard decelerations.

Keywords

• Connected Vehicle
• Driving Volatility
• Traffic Safety
• Human Centered Analysis
• Space-time analysis