Analytic Methods in Accident Research ( IF 12.9 ) Pub Date : 2020-10-20 , DOI: 10.1016/j.amar.2020.100137 Li Song , Yang Li , Wei (David) Fan , Peijie Wu
To systematically account for the spatiotemporal features and unobserved heterogeneity within pedestrian-vehicle crashes, this paper employs the spatiotemporal analysis and hierarchical Bayesian random-effects models to explore the factors contributing to pedestrian-injury severities of pedestrian-vehicle crashes involving single vehicle in North Carolina from 2007 to 2018. Ten spatiotemporal patterns of the crashes are identified by applying an improved spatiotemporal analysis. Significant temporal instability and the spatiotemporal instability of the factors to the pedestrian-injury crashes are identified by the likelihood ratio tests. A hierarchical Bayesian random intercept logit model with random-effects across the spatiotemporal groups is firstly employed for the whole dataset. The comparison between different hierarchical models indicates that addressing random-effects across observations and increasing the number of random parameters could both improve the model performance. Then a hierarchical Bayesian random-effects-only logit model, which allows all parameters to be randomly distributed across observations, is developed to further investigate the unobserved heterogeneity in spatiotemporal segmented datasets. The significant improvements in terms of model fit and the hit accuracy underscore the superiority of the random-effects-only model. The marginal effects of the human, vehicle, crash, locality, roadway, environment, time, and traffic control factors for each spatiotemporal dataset also provide insights into possible inherent reasons for the spatiotemporal instability/tendency of the crash and correlated factors. Meanwhile, specific countermeasures are given to locations especially in which the spatially aggregated patterns of the crashes have new, consecutive, and intensifying temporal tendencies. This study provides a framework for engineers and researchers to identify spatiotemporal patterns of the crashes and explore the factors affecting pedestrian-injury severities especially in those existing crash-prone areas.
中文翻译:
考虑时空模式的行人车祸中行人伤害严重性建模:来自不同分层贝叶斯随机效应模型的见解
为了系统地解决行人车辆碰撞中的时空特征和未观察到的异质性,本文采用时空分析和分层贝叶斯随机效应模型,探讨影响北卡罗来纳州单车行人车辆碰撞严重程度的因素从2007年到2018年。通过应用改进的时空分析方法,确定了十种车祸的时空模式。行人伤害事故的因素在时间上的不稳定性和时空上的不稳定性可以通过似然比检验来确定。首先对整个数据集采用具有时空组随机效应的分层贝叶斯随机截距logit模型。不同层次模型之间的比较表明,解决各观测值之间的随机效应和增加随机参数的数量都可以提高模型性能。然后,开发了一个分层的仅允许贝叶斯随机效应的logit模型,该模型允许所有参数在观测值之间随机分布,以进一步研究时空分段数据集中未观测到的异质性。在模型拟合和命中精度方面的显着改进突出了仅随机效应模型的优越性。每个时空数据集的人为因素,车辆,撞车,地点,道路,环境,时间和交通控制因素的边际影响还提供了对撞车时空不稳定性/趋势和相关因素的可能内在原因的认识。同时,针对特定的对策,特别是在碰撞的空间聚集模式具有新的,连续的和加剧的时间趋势的位置。这项研究为工程师和研究人员提供了一个框架,以识别碰撞的时空模式,并探索影响行人受伤严重程度的因素,尤其是在那些现有的容易发生碰撞的地区。