使用极端价值模型进行行人撞车风险分析:新的见解和证据
Ampereza Ankunda1, Yasir Ali2, Malaya Mohanty1
1School of Civil Engineering, KIIT University, Bhubaneswar, Odisha 751024, India.
Accident; analysis and prevention
|May 16, 2024
概括
极端价值理论 (EVT) 模型可以估计混沌交通中的行人撞车风险. 在异质的交通条件下,高峰超过值的方法和侵占后的时间对预测行人安全最有效.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 道路使用者行为
背景情况:
- 极端价值理论 (EVT) 模型提供了从前体事件 (冲突) 估计交通事故的外推能力.
- 目前对车辆与行人交互的EVT的研究是有限的,特别是在异质的交通和混乱的条件下.
- 在理解适当的EVT模型,决定因素和人类驾驶车辆与行人互动在非信号环境中的冲突措施方面存在差距.
研究的目的:
- 在异质和混乱的交通条件下使用EVT调查行人撞车风险分析.
- 评估不同EVT方法 (区块最大值,峰值超过值) 和冲突措施 (侵占后时间,间隙时间) 的适用性.
- 在复杂的交通环境中确定影响行人撞车风险的关键决定因素.
主要方法:
- 从印度一个繁忙的行人交叉口收集了11小时的视频数据.
- 利用人工智能技术来处理视频,并使用侵犯后时间和间隙时间来描述车辆与行人之间的互动.
- 应用了区块最大值和峰值超过值的EVT方法,结合了解释变量来建模非静止冲突极端.
主要成果:
- 峰值超过值的EVT模型表现出高于区块最大模型的性能.
- 侵占后的时间被确定为一个更合适的冲突措施,而不是车辆与行人互动的间隔时间.
- 两轮车互动时,行人撞车风险增加,群体穿越时降低,并且与车辆速度,行人速度以及短时间后入侵时间的高数量有关.
结论:
- 电动车辆模型适用,并提供合理的估计在异质和混乱的交通条件下历史事故记录.
- 该研究强调了高峰超过值方法和侵占后时间对积极的行人安全管理的有效性.
- 研究结果为在复杂,不纪律的交通环境中制定有针对性的安全干预措施提供了关键的见解.
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