Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States.
Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL 35487, United States.
Accid Anal Prev. 2019 Nov;132:105272. doi: 10.1016/j.aap.2019.105272. Epub 2019 Aug 24.
Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes. The method leverages the space- and time-referenced crash data and powerful computational tools. This study performed non-stationarity tests to verify whether the local correlates of pedestrian injury severity have a significant spatio-temporal variation. Results showed that some variables passed the tests, indicating they have a significantly varying spatio-temporal relationship with the pedestrian injury severity. These factors include the pedestrian age, pedestrian position, crash location, motorist age and gender, driving under the influence (DUI), motor vehicle type and crash time in a day. The spatio-temporally varying correlates of pedestrian injury severity are valuable for researchers and practitioners to localize pedestrian safety improvement solutions in North Carolina. For example, in near future, special attention may be paid to DUI crashes in the city of Charlotte and Asheville, because in such areas DUI-involved crashes are even more likely to cause severe pedestrian injuries that in other areas. More implications are discussed in the paper.
交通事故是人类活动与多元文化、社会经济和地理背景相互作用的结果，具有空间和时间的性质。本研究采用综合时空建模方法来理清可能随空间和时间域变化的事故受伤相关性。具体而言，本研究采用地理和时间加权有序逻辑回归（GTWOLR）来检验机动车事故中行人受伤严重程度的相关因素。该方法利用了具有空间和时间参照的事故数据和强大的计算工具。本研究进行了非平稳性检验，以验证行人受伤严重程度的局部相关性是否存在显著的时空变化。结果表明，一些变量通过了检验，表明它们与行人受伤严重程度存在显著的时空关系。这些因素包括行人年龄、行人位置、事故地点、驾驶员年龄和性别、酒后驾车（DUI）、机动车类型和一天中的事故时间。行人受伤严重程度的时空变化相关因素对于研究人员和从业者在北卡罗来纳州定位行人安全改进解决方案非常有价值。例如，在不久的将来，可能会特别关注夏洛特市和阿什维尔市的 DUI 事故，因为在这些地区，DUI 涉入的事故更有可能导致比其他地区更严重的行人受伤。论文中讨论了更多的影响。
