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.

翻译：本研究利用网联车辆数据分析住宅区道路的超速行为。通过一个可扩展的数据处理流程，对轨迹数据进行处理并补充缺失的限速信息，最终在OpenStreetMap的道路ID层级生成汇总结果。研究发现，激进型与鲁莽型超速行为均呈现高度偏态分布。基于弗吉尼亚州夏洛茨维尔住宅区网联车辆数据的案例研究显示，38%的路段至少发生过一次激进型超速，20%的路段至少发生过一次鲁莽型超速。此外，夜间超速的发生频率是日间的27倍，特定路段的极端违规案例凸显了该问题的严重性。部分路段同时位列激进型与鲁莽型超速的前十名，表明存在高风险住宅区道路。这些发现证实了实施空间干预与行为干预的必要性。本分析为政策制定与规划提供了丰富的基础，是对传统执法与规划工具的有力补充。综上所述，该框架为未来交通安全分析的应用奠定了基础，展现了远程信息处理数据在构建更安全、更宜居社区方面日益增长的应用潜力。