School of Transportation, Southeast University, Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, PR China; School of Transportation, Southeast University, Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, 2 Sipailou, Nanjing 210096, PR China; Urban Planning Group, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands.
School of Transportation, Southeast University, Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, PR China; School of Transportation, Southeast University, Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, 2 Sipailou, Nanjing 210096, PR China.
J Safety Res. 2021 Feb;76:154-165. doi: 10.1016/j.jsr.2020.12.012. Epub 2020 Dec 31.
Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved.
To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ''middle-aged and elderly drivers with low risk of driving violations and high historical crash records," ''drivers with high risk of driving violations and high historical crash records," and ''middle-aged drivers with no driving violations and conviction records." Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.
在事故发生后 30 天内，至少有一名乘客死亡的致命事故会导致大量人员受伤和财产损失，尤其是涉及卡车时。
为了更好地理解与卡车司机相关特征在致命事故中的潜在影响，使用了五年（2012 年至 2016 年）的 Fatality Analysis Reporting System（FARS）数据集进行分析。根据卡车司机的人口统计属性、驾驶违规行为、事故历史和定罪记录，应用潜在类别聚类分析将卡车司机分为三组，即“中年和老年司机，违规风险低，历史事故记录高”、“违规风险高且历史事故记录高的司机”和“无违规和定罪记录的中年司机”。接下来，使用等效死亡率将致命事故严重程度分为三个级别。然后，为每个司机群体开发了部分比例优势（PPO）模型，以确定与事故严重程度相关的风险因素。结果结论：模型估计结果表明，风险因素及其对不同司机群体的影响是不同的。恶劣天气条件、农村地区、弯道、牵引车挂车组合、较重的重量和各种碰撞方式与所有司机群体的事故严重程度显著相关，而酒后或吸毒、疲劳或粗心驾驶等驾驶违规行为仅与高风险群体显著相关，低风险群体的风险因素较少，边际影响较小。实际应用：针对特定的卡车司机群体提出了相应的对策。并且应该更加关注违规风险高且历史事故记录高的司机。
