非机动车道路交通安全研究现状与发展趋势

doi:10.16265/j.cnki.issn1003-3033.2026.02.1404

摘要/Abstract

摘要：

为提升非机动车道路交通安全、降低非机动车道路行驶风险,系统梳理非机动车道路交通安全领域的研究脉络,探索该领域的研究动态与发展方向。基于中国知网(CNKI)和科学网(WOS)核心库,检索2000年1月—2025年3月的相关文献,筛选得到448篇中文文献和860篇英文文献。运用CiteSpace软件,对文献进行关键词共现、聚类分析,进而归纳出核心研究热点与发展趋势。结果表明:该领域的研究规模显著增长,研究热点主要集中于非机动车骑行行为特征、道路交通事故成因、混合交通冲突机制以及与道路交通管理政策等4大主题。在未来研究中,需进一步拓展多元场景研究并深化多模态数据的融合应用;与此同时,计算机视觉与机器学习等技术正逐步成为推动该领域方法创新的关键支撑。

关键词: 非机动车, 道路交通安全, 交通事故, 交通冲突, 共现网络

Abstract:

The situation of road traffic safety of non-motorized vehicles remains severe, and extensive research has been conducted in China and abroad. To comprehensively analyze the research status and prospect, 448 Chinese papers and 860 English papers from 2000 to 2025 were selected from China National Knowledge Infrastructure (CNKI) and Web of Science (WOS). Subsequently, the papers were analyzed for keyword co-occurrence and cluster by CiteSpace, and on this basis, the research hotspots and prospect were summarized. The results indicate that current research shows a significant growth trend, and primarily focuses on four areas: risk of riding behaviors, road traffic accidents, mixed traffic flow conflicts, and traffic management strategies. Future studies should expand into diverse scenarios and deepen the application of multimodal data, with methodological support of computer vision technologies and machine learning algorithms to enhance research capabilities.

Key words: non-motorized vehicles, road traffic safety, traffic accidents, traffic conflicts, co-occurrence network

中图分类号:

X951交通运输安全

何杰, 陈维雨, 陈昊泽, 方之茗, 秦鹏程. 非机动车道路交通安全研究现状与发展趋势[J]. 中国安全科学学报, 2026, 36(2): 52-65.

HE Jie, CHEN Weiyu, CHEN Haoze, FANG Zhiming, QIN Pengcheng. Research status and prospect of road traffic safety of non-motorized vehicles[J]. China Safety Science Journal, 2026, 36(2): 52-65.

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文献	非机动车行为	研究方法	研究场景
[11]	载客、不佩戴头盔、闯红灯、逆行等	观察研究	苏州市交叉口电动车行为
[12][16]	不按规定车道骑行、超速行驶	基于轨迹数据	长沙市共享电动车行为
[13][17]	闯红灯	观察研究	南京、北京交叉口电动车和自行车行为
[15]	使用手机	试验研究	荷兰自行车行为
[18]	不按规定车道骑行、超速、违反交通信号灯等	基于GPS数据	美国共享自行车和电动车行为
[20]	超速行驶	试验研究	德国自行车和电动车行为
[26]	不佩戴头盔、占用人行道及机动车道等	观察研究	中国共享自行车行为
[28]	不佩戴头盔、闯红灯、使用手机等	观察研究	澳大利亚配送自行车与私人自行车行为

要素	影响因素	作用机制
人^[45-46]	生理与心理特征,如年龄、性别、驾龄、风险感知、安全认知等	骑行者的生理与心理状态会影响骑行的决策及行为,如疲劳、分心或风险感知偏差可能导致危险行为发生,增加事故风险
车^[47]	车辆类型、行驶状态、安全状况等	车辆性能与状态直接影响行驶安全,如超速或关键部件(如制动、转向)故障等容易导致交通事故的发生
路^[48]	道路线形、路段类型、道路物理隔离设施等	道路应提供安全、可靠的行驶环境,路权混行、车流密度大的区域(如交叉口、缺乏隔离设施的路段等)容易引发交通冲突及事故
环境^[49]	天气、光照、环境、基础设施等	不良的气象条件(如雨、雪、雾等)及复杂的建成环境(如照明不足、路侧停车等)会影响骑行者的感知与判断能力,增加事故发生概率

文献	模型	因素					研究场景
文献	模型	人	车	路	环境	事故	研究场景
[51]	有序Logit模型	√	√	√	√		机动车与非机动车交通事故
[52]	混合Logit模型	√		√	√		自行车与机动车事故
[54]	随机参数Logit模型	√	√	√	√		交叉口自行车事故
[55]	随机参数广义有序Probit模型	√	√	√	√	√	电动自行车事故
[57]	贝叶斯网络	√	√	√	√	√	自行车事故
[58]	随机森林及随机参数Logit模型	√	√	√	√		自行车事故
[59]	XGBoost、LightGBM模型	√	√	√		√	电动自行车与机动车事故
[60]	随机森林及SHAP可解释模型	√		√	√		自行车事故

类别	指标
避险行为指标	是否存在避险行为,如转向、刹车等
空间/时间指标	冲突时间(Time to collision, TTC) 后侵入时间(Post encroachment time, PET) 时间优势(Time Advance, TA) 修正碰撞时间(Modified Time To Collision, MTTC) 停车距离比例(Proportion of Stopping Distance, PSD)
与机动车运动状态相关指标	安全减速度(Deceleration To Safety Time, DST) 避免碰撞的减速率(Deceleration rate to avoid the crash, DRAC)