USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States.
USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States.
J Safety Res. 2021 Feb;76:184-196. doi: 10.1016/j.jsr.2020.12.008. Epub 2020 Dec 29.
With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend.
To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects.
The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend.
在过去十年中，行人在所有交通死亡事故中的死亡比例呈上升趋势，因此迫切需要识别和调查行人与车辆碰撞的热点区域，这些区域的事故数量呈上升趋势。
为了识别具有聚集空间模式和上升时间模式（即具有上升趋势的热点）的行人-车辆碰撞地点，本文首先使用平均最近邻和空间自相关检验来分别确定热点的网格距离和邻域距离。然后，利用时空分析中的 Getis-Ord Gi* 指数和 Mann-Kendall 趋势检验，从 2007 年到 2018 年，在美国北卡罗来纳州识别出具有年度上升趋势的行人-车辆碰撞热点。考虑到碰撞数据的未观察到的异质性，提出了一个具有类内随机参数的潜在类别模型，以识别每个类别的具体促成因素，并探索类内的异质性。基于边际效应，检测和分析了行人、车辆、碰撞类型、地点、道路、环境、时间和交通控制特征的显著因素。
类间的异质结果和类内检测到的随机参数变量进一步表明了潜在类别随机参数模型的优越性。
本文为研究人员和工程师提供了一个框架，用于识别考虑时空模式的碰撞热点，并考虑未观察到的异质性对碰撞的贡献因素。此外，研究结果为制定缓解行人受伤的对策提供了具体指导，以应对具有上升趋势的行人-车辆碰撞热点。
