交通违规分析:识别危险区域和常见违规行为
El Mehdi Ben Laoula1, Omar Elfahim1, Marouane El Midaoui2
12IACS Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco.
Heliyon
|September 4, 2023
概括
这项研究确定了高风险的交通违规区域和超速等常见违法行为. K-means集群优化执法策略,以提高道路安全并减少事故.
科学领域:
- 公共卫生 公共卫生
- 运输安全运输安全
- 数据科学数据科学数据科学
背景情况:
- 道路交通事故是一个重大的公共卫生问题,导致死亡和经济损失.
- 有效的道路安全措施对于减少事故发生率和严重程度至关重要.
- 需要有针对性的执法策略来解决普遍存在的交通违法行为.
研究的目的:
- 提出一个增量道路安全战略,优先考虑执法.
- 识别高风险区域和常见的交通违法行为.
- 优化执法机构的资源配置.
主要方法:
- 分析不同地区的交通违规数据.
- 使用科尔莫戈罗夫-斯米尔诺夫 (KS) 测试,将地区违规数据与整体平均值进行比较.
- 评估各种集群优化技术,包括k-means,以识别违规集群.
主要成果:
- 确定交通违规风险较高的特定地区.
- 检测常见的违规行为,如超速,登记,驾照,安全带,驾驶在影响下,和电话使用.
- K-means集群在识别违规集群以优化执法方面表现出卓越的表现.
结论:
- 执法部门可以利用这些发现,专注于高风险区域和普遍的违法行为.
- 优化执法策略可以带来更高效的资源利用.
- 该研究提供了一种数据驱动的方法,以提高整体道路安全并减轻事故后果.
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