基于CIDAS数据与可解释模型的行人交通事故风险识别

Risk Recognition of Pedestrian Traffic Accidents Based on CIDAS Data and Interpretable Model

行人道路交通事故是一种常见的交通事故,为了构建有效的行人交通安全防治体系,论文使用中国事故深度调查(CIDAS)数据集进行分析研究。采用多次重复的K折交叉验证评估,并确认随机森林模型在该数据集上具有统计学功效后,利用基于排列的特征重要性算法对影响行人交通事故的特征进行了量化分析。随后对重要事故特征的数据进行统计,并使用卡方检验确定随机性的影响。研究表明,事故参与人员数、行人年龄段、事故发生时间与道路最高允许车速是影响行人交通事故后果的最重要特征。整体趋势表明事故参与人员数越多,事故后果越严重;对于13岁及以上的人群,行人年龄越大发生事故的后果也更严重;在凌晨0:00-4:00发生的事故中,事故的严重程度明显高于其他时间段;在限速为80 km/h及以上的道路上发生事故的后果更严重。

Pedestrian road traffic accidents are a common type of traffic accidents.In order to build an effective pedestrian traffic safety prevention and control system,the China in-depth accident study(CIDAS)data is used for analysis and research.After using repeated rounds of K-fold cross-validation to evaluate and confirm that the random forest model has statistical power on this data,the permutation feature importance algorithm is used to quantify the features that affect pedestrian traffic accidents.The data of important features are then statistically analyzed and chi-squared test is used to determine the effect of randomness.The research shows that the number of accident participants,age group,accident occurrence time and maximum speed limit are the most important features affecting the consequences of pedestrian traffic accidents.The overall trend shows that the more people involved in the accident,the more serious the consequences of the accident;for people aged 13 and above,the older the pedestrian,the more serious the consequences of the accident;in the accidents that occurred between 0:00 to 4:00 in the morning,the severity of the accidents is significantly higher than that in other time periods;the consequences of accidents on roads with a speed limit of 80 km/h and above are more serious.