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

TL;DR

The study develops a scalable, trajectory-based framework to quantify residential speeding using high-frequency connected vehicle data in Charlottesville and Albemarle County. It augments missing speed limits, classifies trajectory points into aggressive ($\geq$ added speed limit $+ 10$ mph) and reckless ($\geq$ added speed limit $+ 20$ mph) categories, and aggregates results at the OpenStreetMap Way ID level. Results reveal a highly skewed distribution: 38% of residential segments show at least one aggressive speeding event and 20% show at least one reckless event, with nighttime speeding markedly more prevalent. The approach provides a data-driven, per-road-segment view of speeding that supports targeted enforcement and planning, and establishes a foundation for future integration with crash data and exposure measures to enhance traffic safety analytics in livable communities.

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.

Figures (2)

• Figure 1: Cumulative Distribution of the aggressive speeding % for each Way ID
• Figure 2: Cumulative Distribution of the reckless speeding % for each Way ID