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

2026-01-16

AI Summary

This study addresses the frequent occurrence of speeding in residential neighborhoods by proposing a novel, scalable analytical framework that integrates large-scale connected vehicle trajectory data with OpenStreetMap road network identifiers. By augmenting speed limit information, performing high-precision map matching, and classifying speeding behaviors into aggressive and dangerous categories, the approach enables fine-grained, segment-level quantification of speeding risk. Results reveal that 38% and 20% of road segments exhibit aggressive and dangerous speeding, respectively, with nighttime incidents occurring up to 27 times more frequently than during daytime hours. The analysis further identifies multiple road segments where high-risk behaviors overlap spatially. This methodology establishes a new paradigm for non-enforcement-based traffic safety interventions, offering both empirical evidence and a reproducible foundation for targeted mitigation strategies in residential areas.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

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.

Problem

• residential speeding
• aggressive speeding
• reckless speeding
• connected vehicle data
• traffic safety

Innovation

• connected vehicle data
• speeding behavior analysis
• scalable data pipeline
• residential road safety
• telematics analytics