时空约束下的道路交通事故与违法行为关联规则研究

doi:10.16097/j.cnki.1009-6744.2025.03.022

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (3): 246-254.DOI: 10.16097/j.cnki.1009-6744.2025.03.022

时空约束下的道路交通事故与违法行为关联规则研究

方腾源^1a,1b，徐峰祥^*1a,1b，朱其茂²，邹震^1a,1b

1. 武汉理工大学，a.现代汽车零部件技术湖北省重点实验室；b.汽车零部件技术湖北省协同创新中心，武汉430070； 2. 柳州赛克科技发展有限公司，广西柳州545005

收稿日期:2025-02-20 修回日期:2025-03-16 接受日期:2025-03-24 出版日期:2025-06-25 发布日期:2025-06-21
作者简介:方腾源(1996—)，男，湖北随州人，博士生。
基金资助:
国家自然科学基金(52475277)。

Association Rules Between Urban Road Traffic Accidents and Violations Considering Temporal and Spatial Constraints

FANG Tengyuan^1a,1b, XU Fengxiang^*1a,1b, ZHU Qimao², ZOU Zhen^1a,1b

1a. Hubei Key Laboratory of Advanced Technology of Automotive Components; 1b. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China; 2. Liuzhou Saike Technology Development Co Ltd, Liuzhou 545005, Guangxi, China

Received:2025-02-20 Revised:2025-03-16 Accepted:2025-03-24 Online:2025-06-25 Published:2025-06-21
Supported by:
National Natural Science Foundation of China (52475277)。

摘要/Abstract

摘要： 为研究道路交通违法行为与交通事故之间的关联性，本文构建时空双维度约束方法，分析不同违法行为对交通事故的诱发特性。以2023—2024年北京市城区的交通事故数据为基础，结合电子执法系统违法数据，关联出2338条与交通事故相关的违法行为数据，避免了传统报告中对违法行为的主观判断带来的偏差，并通过FP-growth(Frequent Pattern growth)算法挖掘出涉及5类交通事故和4类交通违法行为的18条强关联规则。研究结果表明：交通事故和违法行为的关联数据在空间上分布较为均匀，时间上主要集中在7:30-22:30，并在早晚高峰期间达到峰值；机动车与机动车事故多由在雨天、拥堵环境和高峰时段的闯红灯行为引起，其置信度高达1.000，提升度为1.689；机动车与非机动车事故多发生于教育区和居民区，受违停行为影响显著，置信度为0.495，提升度达2.578；机动车单方事故同样主要与违停行为相关，其提升度高达8.696。关联规则可为优化执法措施、智能信号控制、道路规划优化等提供决策支持，并为其他城市交通管理提供参考，提升道路安全水平。

关键词: 城市交通, 关联规则, FP-growth算法, 交通事故数据, 交通违法

Abstract: To investigate the correlation between road traffic violations and traffic accidents, this paper proposes a spatiotemporal constraint-based approach to analyze the contributory characteristics of various violations in triggering traffic accidents. Utilizing traffic accident data from Beijing city in 2023 and 2024, in conjunction with violation records from the electronic enforcement system, this study analyzed 2338 traffic violations associated with accidents. This data-driven approach eliminates the subjective biases inherent in traditional reports. Furthermore, by applying the Frequent Pattern Growth (FP-growth) algorithm, the study identified 18 strong association rules linking five types of traffic accidents to four categories of traffic violations. The results indicate that the spatial distribution of correlated data between traffic accidents and violations is relatively uniform. The incidents are primarily concentrated between 7:30 AM and 10:30 PM, peaking during the morning and evening rush hours. Collisions between motor vehicles and non-motor vehicles primarily occur in educational and residential areas, with illegal parking behaviors significantly influencing the occurrence of the accidents. These collisions exhibit a confidence level of 0.495 and a lift value of 2.578. Single-vehicle accidents are predominantly associated with illegal parking behaviors, exhibiting a lift value as high as 8.696. This highlights the substantial threat that illegal parking poses to traffic order and safety. The derived association rules offer decision support for precision law enforcement, intelligent signal control, and road network optimization. The study results might also provide reference for urban traffic management in other cities, to improve traffic safety.

Key words: urban traffic, association rules, FP-growth algorithm, traffic accident data, traffic violations

中图分类号:

U491.31

方腾源, 徐峰祥, 朱其茂, 邹震. 时空约束下的道路交通事故与违法行为关联规则研究[J]. 交通运输系统工程与信息, 2025, 25(3): 246-254.

FANG Tengyuan, XU Fengxiang, ZHU Qimao, ZOU Zhen. Association Rules Between Urban Road Traffic Accidents and Violations Considering Temporal and Spatial Constraints[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(3): 246-254.

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时空约束下的道路交通事故与违法行为关联规则研究

Association Rules Between Urban Road Traffic Accidents and Violations Considering Temporal and Spatial Constraints

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