Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
School of Mathematical and Computer Sciences, Heriot-Watt University, Dubai Campus, UAE.
Int J Inj Contr Saf Promot. 2024 Sep;31(3):499-507. doi: 10.1080/17457300.2024.2349554. Epub 2024 May 7.
This study simultaneously modelled the injury severity of motorcycle riders and their pillion passengers and determine the associated risk factors. The analysis is based on motorcycle crashes data in Ashanti region of Ghana spanning from 2017 to 2019. The study implemented bivariate ordered probit model to identify the possible risk factors under the premise that the injury severity of pillion passenger is endogenously related to that of the rider in the event of crash. The model provides more efficient estimates by considered the common unobserved factors shared between rider and pillion passenger. The result shows a significant positive relationship between the two injury severities with a correlation coefficient of 0.63. Thus, the unobservable factors that increase the probability of the rider to sustain more severe injury in the event of crash also increase that of their corresponding pillion passenger. The rider and their pillion passenger injury severities have different propensity to some of the risk factors including passengers' gender, day of week, road width and light condition. In addition, the study found that time of day, weather condition, collision type, and number of vehicles involved in the crash jointly influence the injury severity of both rider and pillion passenger significantly.
本研究同时对摩托车骑手及其后座乘客的伤害严重程度进行建模，并确定相关的风险因素。该分析基于加纳阿散蒂地区 2017 年至 2019 年的摩托车碰撞数据。研究采用双变量有序概率模型，在假设后座乘客的伤害严重程度与骑手在碰撞事件中的伤害严重程度存在内生关系的前提下，确定可能的风险因素。该模型通过考虑骑手和后座乘客之间共同存在的不可观测因素，提供了更有效的估计。结果表明，两种伤害严重程度之间存在显著的正相关关系，相关系数为 0.63。因此，增加骑手在碰撞事件中遭受更严重伤害概率的不可观测因素也会增加其对应后座乘客的受伤概率。骑手及其后座乘客的伤害严重程度对一些风险因素的倾向不同，包括乘客的性别、星期几、道路宽度和照明条件。此外，研究还发现，一天中的时间、天气条件、碰撞类型以及事故中涉及的车辆数量共同显著影响骑手和后座乘客的伤害严重程度。
