单车事故常发于车流量较小、道路条件较差的时段和路段，其致死率明显高于交通事故平均死亡率。为了探究影响单车事故严重程度的关键因素，基于中国部分地区2015—2019年单车交通事故数据，从人、车、路、环境等方面选取24个事故影响因素，通过对数似然比检验事故数据的时间稳定性，发现事故数据存在时间不稳定性，应将其划分为5个年份分别建模，构建考虑均值方差异质性的随机参数Logit模型。对事故因素变量的边际效应进行对比，结果表明：5个不同年份的时间稳定性模型均具有较好的拟合效果；模型能够有效地捕捉未观测到的异质性，且不同时间模型下捕捉的参数也具有随机性。模型参数估计结果表明：路口路段类型、客车、摩托车和事故责任共4个事故因素具有时间稳定性，其他事故影响因素仅在个别年份中具有显著影响；防护设施事故因素也具有时间稳定性；客车、摩托车、车辆前照灯状态、车辆安全状况、撞固定物和事故责任等事故因素变量会显著增加单车事故中人员死亡的可能性；防护设施类型、能见度大于100 m等变量会显著降低受伤严重程度。

Single-vehicle accidents often occur in time periods with low traffic volume and poor road conditions, and their fatality rate is significantly higher than the average mortality rate of traffic accidents. To explore the key factors affecting the accident severity degree of single-vehicle accidents, based on the single-vehicle traffic accident data from 2015 to 2019 in some regions of China, this paper selected 24 accident-influencing factors from aspects such as people, vehicles, roads, and the environment. The temporal stability of the accident data was tested by the log-likelihood ratio test which found that there was temporal instability in the accident data, so it should be divided into five years for separate modeling. Then, a random parameter Logit model considering the mean-variance heterogeneity was constructed. The marginal effects of accident factor variables were compared. The results show that the temporal stability models of the five different years all have good fitting effects. The model can effectively capture the unobserved heterogeneity, and the captured parameters under different time models also exhibit randomness. The results of model parameter estimation indicate that four accident factors, namely the type of intersection and road section, passenger cars, motorcycles, and accident liability, have temporal stability, while other accident-influencing factors only have significant effects in individual years. The accident factor of protective facilities also have temporal stability. Accident factor variables such as passenger cars, motorcycles, the state of vehicle headlights, vehicle safety conditions, hitting fixed objects, and accident liability will significantly increase the possibility of casualties in single-vehicle accidents. Variables such as the type of protective facilities and visibility greater than 100 meters will significantly reduce the severity of injuries.