Analyzing Residential Speeding Using Connected Vehicle Data: A Case Study in Charlottesville, VA Area

TL;DR

This study leverages connected vehicle data to analyze speeding behaviors on residential roads in Charlottesville, VA, revealing high prevalence of aggressive and reckless speeding, especially at night, and highlighting high-risk segments for targeted safety interventions.

Contribution

Introduces a scalable pipeline for analyzing connected vehicle data to identify speeding patterns and high-risk segments on residential roads, enhancing traffic safety analytics.

Findings

38% of segments had aggressive speeding instances

20% of segments had reckless speeding instances

Nighttime speeding is 27 times more prevalent than daytime

Abstract

This study uses connected vehicle data to analyze speeding behavior on residential roads. A scalable pipeline processes trajectory data and supplements missing speed limits to generate summaries at OpenStreetMap's way ID level. The findings reveal a highly skewed distribution of both aggressive and reckless speeding. Based on a case study of Charlottesville, VA's connected vehicle data on residential roads, we found that 38% of segments had at least one instance of aggressive speeding, and 20% had at least one instance of reckless speeding. In addition, night time speeding is 27 times more prevalent than day time, and extreme violations on specific road segments highlight how severe the issue can be. Several segments rank among the top 10 for both aggressive and reckless speedings, indicating that there exist high-risk residential roads. These findings support the need for both spatial and behavioral interventions. The analysis provides a rich foundation for policy and planning, offering a valuable complement to traditional enforcement and planning tools. In conclusion, this framework sets the foundation for future applications in traffic safety analytics, demonstrating the growing potential of telematics data to inform safer, more livable communities.