基于CIDAS数据与集成学习的电动两轮车骑行者伤害致因分析

电动两轮车保有量持续增长导致相关的事故伤害日益严重。为研究电动两轮车-机动车碰撞事故中电动两轮车骑行者受伤程度的影响因素，以中国事故深度调查(CIDAS)数据集中的1 246起电动两轮车-机动车事故案例为基础，对比随机森林、XGBoost和LightGBM这3种集成学习模型性能，基于准确率等指标选用性能最优的LightGBM模型进行电动车骑行者受伤严重程度预测。结合SHAP可解释方法，进一步分析发现自变量与因变量之间存在明显的非线性关系:电动两轮车骑行者抛出距离对死亡的影响存在明显的阈值效应，电动两轮车骑行者被抛出距离小于5 m时，不易发生死亡事故，超过5 m时，抛出距离和死亡风险呈正相关；事故发生地为市区外或公路上以及与载重物车辆相撞能显著增加电动两轮车事故中骑行者的死亡风险；电动两轮车不加装脚蹬、座位高度大于70 cm、车把宽度为61~65 cm、车把设计形式为向后弯曲或牛角状等因素可降低死亡风险；与电动两轮车骑行者相关的降低死亡风险的因素包括女性、年龄在30~50岁及对事故发生地环境更为熟悉。 

An Analysis of Factors Affecting Injury of Electric Two-wheeler Riders Based on CIDAS Data and Ensemble Learning

A growing use of electric two-wheelers leads to an increasing number of serious accidents. In order to study the factors affecting injury severity of electric two-wheeler riders within the collisions involving electric two-wheelers, three integrated learning models, i.e. random forest, XGBoost, and LightGBM, are developed and compared based on 1 246 electric two-wheelers and motor vehicle accidents collected from the China Depth of Accident Investigation(CIDAS)dataset. According to the accuracy and other indicators, the LightGBM model is chosen for its best performance to predict the severity of injury suffered by electric vehicle riders. With SHAP-method analysis, a nonlinear relationship between independent variables and dependent variables is observed. There is an evident threshold for the impacts of the throwing distance of the electric two-wheeler riders on the risk of death. Electric two-wheeler riders are not susceptible to death accidents when the throwing distance is less than 5 meters. When the throwing distance exceeds 5 meters, there is a positively correlation between throwing distance and risk of death. Accidents occur in outside urban areas or on highways and collisions with heavy vehicles significantly increase the risk of death to riders involved in accidents. Factors like no pedal, seat height greater than 70 cm, handlebar width of 61~65 cm, and handlebar design of backward bending or horn shape can reduce the risk of death. Being female, age 30~50, and familiar with the location are associated with a lower risk of death.