利用基于人工智能的视频分析建立非平稳双变量极值模型,按严重程度估算信号灯路口的实时行人碰撞风险

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Hassan Bin Tahir, Md Mazharul Haque
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引用次数: 0

摘要

车辆与行人之间的碰撞事故一般都很严重,这是因为与车辆乘员相比,行人更容易受到伤害。然而,在应用交通冲突技术进行主动安全评估的领域中,按严重程度估算行人碰撞风险的方法尚未得到足够重视。本研究提出了一个新颖的分析框架,用于在信号周期层面按严重程度估算实时行人碰撞风险,同时纳入时变外生变量的影响。具体来说,该研究采用非平稳双变量极值模型,对侵占后时间和 Delta-V 进行联合建模,按严重程度估算单个信号周期的实时行人碰撞风险。所提出的框架在澳大利亚昆士兰州三个信号灯路口收集的 144 小时视频数据中进行了测试。与这些信号灯路口严重和非严重行人碰撞的历史碰撞记录相比,发现所开发的双变量极值模型能可靠地预测严重和非严重行人碰撞频率。结果表明,每个信号周期内的行人冲突频率和信号周期内的行人平均速度与实时行人碰撞风险有关。此外,每个信号周期的行人冲突频率和每个信号周期的平均车速与非平稳双变量极值模型的交互严重性分量相关。按严重程度主动估计行人碰撞风险的建议有助于为易受伤害的道路使用者设计具有时间敏感性的应对措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A non-stationary bivariate extreme value model to estimate real-time pedestrian crash risk by severity at signalized intersections using artificial intelligence-based video analytics

Vehicle-pedestrian crashes are generally severe due to the vulnerability of pedestrians compared to the occupants of vehicles. However, the estimation of pedestrian crash risk by severity has not been given adequate attention in the field of proactive safety assessments applying traffic conflict techniques. This study proposes a novel analytical framework to estimate real-time pedestrian crash risk by severity at the signal cycle level while incorporating the effect of time-varying exogenous variables. Specifically, the study applies a non-stationary bivariate extreme value model to jointly model the post encroachment time and Delta-V for estimating real-time pedestrian crash risk by severity at individual signal cycles. The proposed framework is tested on 144 h of video data collected from three signalized intersections in Queensland, Australia. The developed bivariate extreme value model has been found to reliably predict severe and non-severe pedestrian crash frequencies compared to the historical crash records of severe and non-severe pedestrian crashes at those signalized intersections. Results suggest that the frequency of pedestrian conflicts per signal cycle and average pedestrian speed in a signal cycle are associated with real-time pedestrian crash risks. In addition, pedestrian conflicts per signal cycle and average vehicle speed per cycle were associated with the interaction severity component of the non-stationary bivariate extreme value model. The proposed proactive estimation of pedestrian crash risk by severity levels can help design time-sensitive countermeasures for vulnerable road users.

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来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
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