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[1]朱兴林,王诺,刘泓君,等.基于可解释集成学习的电动自行车骑行风险识别及致因解析*[J].中国安全生产科学技术,2026,22(4):88-97.[doi:10.11731/j.issn.1673-193x.2026.04.011]
　ZHU Xinglin,WANG Nuo,LIU Hongjun,et al.Risk identification and causal analysis of electric bike riding using explainable ensemble learning[J].Journal of Safety Science and Technology,2026,22(4):88-97.[doi:10.11731/j.issn.1673-193x.2026.04.011]
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基于可解释集成学习的电动自行车骑行风险识别及致因解析*

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
22
期数:
2026年4期
页码:
88-97
栏目:
安全工程技术
出版日期:
2026-04-30

文章信息/Info

Title:
Risk identification and causal analysis of electric bike riding using explainable ensemble learning
文章编号:
1673-193X(2026)-04-0088-10
作者:
朱兴林; 王诺; 刘泓君; 吴隽朝; 王光东; 郭瑞
(1.新疆农业大学 交通与物流工程学院，新疆 乌鲁木齐 830052;
2.新疆农业大学 新疆交通运输与物流工程重点实验室，新疆 乌鲁木齐 830052；
3.新疆道路交通安全工程技术研究中心，新疆 乌鲁木齐 830094）
Author(s):
ZHU Xinglin;  WANG Nuo;  LIU Hongjun;  WU Junchao;  WANG Guangdong;  GUO Rui
(1.School of Transportation & Logistics Engineering,Xinjiang Agricultural University,Urumqi Xinjiang 830052,China;
2.Key Laboratory of Transportation and Logistics Engineering in Xinjiang,Xinjiang Agricultural University,Urumqi Xinjiang 830052,China;
3.Xinjiang Engineering Technology Research Center for Road Traffic Safety,Urumqi Xinjiang 830094,China）
关键词:
交通安全; 电动自行车; 骑行风险识别; Stacking集成学习; 部分依赖图算法; SHAP算法
Keywords:
traffic safety;  electric bicycle;  risk identification;  stacking ensemble learning;  partial dependence plot;  SHAP algorithm
分类号:
X951
DOI:
10.11731/j.issn.1673-193x.2026.04.011
文献标志码:
A
摘要:
为实现对城市电动自行车骑行风险的识别与预防，基于自然骑行实验构建了包含骑行者违规行为、生理特征、车辆运行特征、道路条件、土地利用特征的多源异构数据库，依据车辆运行数据将骑行风险划分为低、中、高3个等级用于风险标签，采用Stacking集成学习算法构建骑行风险识别模型，结合部分依赖图(partial dependence plot，PDP)算法与SHAP值分析(SHapley Additive exPlanations,SHAP)等解释方法探讨各特征对骑行风险的影响规律。研究结果表明：构建的骑行风险识别模型准确率为94.26%；影响骑行风险的关键因素依次为闯红灯率、车速均值、逆行发生率、加速度标准差、航向角标准差、心率平均值；车辆运行特征与道路条件、土地利用特征的交互作用对骑行风险具有差异化影响；合理设置非机动车道宽度与机非分隔形式，均能有效提升骑行安全性。研究结果可为实现城市电动自行车骑行安全风险防控提供参考。
Abstract:
In order to achieve the identification and prevention of urban electric bicycle riding risks,a multi-source heterogeneous database is constructed based on naturalistic riding experiments,containing rider violation behaviors,physiological characteristics,vehicle operational parameters,road conditions,and land-use characteristics.Riding risk is classified into three levels (low,medium,and high) based on vehicle operational data to create risk labels.A Stacking ensemble learning algorithm is employed to construct a riding risk identification model.Interpretability methods such as the partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) are integrated to investigate the influence patterns of various features on riding risk.Results indicate that the proposed model achieves an accuracy of 94.26%.The key factors affecting riding risk,in descending order of importance,are red-light running rate,average speed,incidence of riding against traffic,standard deviation of acceleration,standard deviation of heading angle,and average heart rate.The interaction between vehicle operational characteristics and road or land-use features exerts a differentiated impact on riding risk.Appropriately setting the width of non-motorized lanes and the form of motorized-non-motorized separation can effectively enhance riding safety.The findings provide actionable insights for the prevention and control of safety risks in urban electric bicycle riding.

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备注/Memo

备注/Memo:
收稿日期： 2025-10-17
* 基金项目： 新疆维吾尔自治区自然科学基金项目(2024D01A64)；新疆维吾尔自治区高校基本科研业务费科研项目(XJEDU2024P040)
作者简介： 朱兴林，博士，教授，主要研究方向为交通安全与环境。
更新日期/Last Update: 2026-04-29