Research status and prospect of road traffic safety of non-motorized vehicles

doi:10.16265/j.cnki.issn1003-3033.2026.02.1404

Abstract

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

CLC Number:

X951交通运输安全

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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References 88

[1]	中华人民共和国交通运输部. 绿色交通“十四五”发展规划(交规划发 [2021] 104号)[Z]. [2025-06-23]. https://xxgk.mot.gov.cn/2020/jigou/zhghs/202201/t20220121_3637584.html.
[2]	GB17761—2018, 电动自行车安全技术规范[S].
	GB17761-2018, Safety technical specification for electric bicycle[S].
[3]	中华人民共和国统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2024:765.
[4]	吕能超, 王玉刚, 周颖, 等. 道路交通安全分析与评价方法综述[J]. 中国公路学报, 2023, 36(4):183-201. doi: 10.19721/j.cnki.1001-7372.2023.04.016
	LYU Nengchao, WANG Yugang, ZHOU Ying, et al. Review of road traffic safety analysis and evaluation methods[J]. China Journal of Highway and Transport, 2023, 36(4):183-201. doi: 10.19721/j.cnki.1001-7372.2023.04.016
[5]	PRATI G, PUCHADES V M, DE A M, et al. Factors contributing to bicycle-motorized vehicle collisions: a systematic literature review[J]. Transport Reviews, 2018, 38(2):184-208. doi: 10.1080/01441647.2017.1314391
[6]	POUDEL N, SINGLETON P A. Bicycle safety at roundabouts: a systematic literature review[J]. Transport Reviews, 2021, 41(5):617-642. doi: 10.1080/01441647.2021.1877207
[7]	LI Yuping, CHEN Qi, MA Qiang, et al. Injuries and risk factors associated with bicycle and electric bike use in China: a systematic review and meta-analysis[J]. Safety Science, 2022, 152:DOI:10.1016/j.ssci.2022.105769.
[8]	ZOU Xin, YUE Wenlong, HAI Lewu. Visualization and analysis of mapping knowledge domain of road safety studies[J]. Accident Analysis and Prevention, 2018, 118:131-145. doi: 10.1016/j.aap.2018.06.010
[9]	韦漠. 基于CiteSpace的我国道路交通事故影响因素文献计量与可视化知识图谱分析[J]. 安全与环境工程, 2024, 31(2):17-33.
	WEI Mo. Bibliometric and visual knowledge graph analysis of road traffic accident influencing factors in China based on CiteSpace[J]. Safety and Environmental Engineering, 2024, 31(2):17-33.
[10]	陈悦, 陈超美, 刘则渊, 等. CiteSpace知识图谱的方法论功能[J]. 科学学研究, 2015, 33(2):242-253.
	CHEN Yue, CHEN Chaomei, LIU Zeyuan, et al. The methodology function of CiteSpace mapping knowledge domains[J]. Studies in Science of Science, 2015, 33(2):242-253.
[11]	DU Wei, YANG Jie, POWIS B, et al. Understanding on-road practices of electric bike riders: an observational study in a developed city of China[J]. Accident Analysis and Prevention, 2013, 59:319-326. doi: 10.1016/j.aap.2013.06.011
[12]	ZHANG Xiaolong, ZHAO Xiaohua, BIAN Yang, et al. Interactive effects analysis of road, traffic, and weather characteristics on shared e-bike speeding risk: a data-driven approach[J]. Accident Analysis and Prevention, 2024, 207:DOI:10.1016/j.aap.2024.107755.
[13]	GUO Yanyong, LI Zhibin, WU Yao, et al. Exploring unobserved heterogeneity in bicyclists' red-light running behaviors at different crossing facilities[J]. Accident Analysis and Prevention, 2018, 115:118-127. doi: 10.1016/j.aap.2018.03.006
[14]	TAIT C, BEECHAM R, LOVELACE R, et al. Contraflows and cycling safety: evidence from 22 years of data involving 508 one-way streets[J]. Accident Analysis and Prevention, 2023, 179:DOI:10.1016/j.aap.2022.106895.
[15]	DE W D, LEWIS-EVANS B, JELIJS B, et al. The effects of operating a touch screen smartphone and other common activities performed while bicycling on cycling behaviour[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 22:196-206. doi: 10.1016/j.trf.2013.12.003
[16]	ZHANG Xiaolong, BIAN Yang, ZHAO Xiaohua, et al. The association of road, traffic, and environmental factors with wrong-way riding behavior on shared e-bikes[J]. Journal of Cleaner Production, 2024,470:DOI:10.1016/j.jclepro.2024.143294.
[17]	WU Changxu, YAO Lin, ZHANG Kan. The red-light running behavior of electric bike riders and cyclists at urban intersections in China: an observational study[J]. Accident Analysis and Prevention, 2012, 49:186-192. doi: 10.1016/j.aap.2011.06.001
[18]	LANGFORD B C, CHEN Jiaoli, CHERRY C R. Risky riding: naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders[J]. Accident Analysis and Prevention, 2015, 82:220-226. doi: 10.1016/j.aap.2015.05.016
[19]	SCHLEINITZ K, PETZOLDT T, KROELING S, et al. (E-)Cyclists running the red light: the influence of bicycle type and infrastructure characteristics on red light violations[J]. Accident Analysis and Prevention, 2019, 122:99-107. doi: 10.1016/j.aap.2018.10.002
[20]	SCHLEINITZ K, PETZOLDT T, FRANKE-BARTHOLDT L, et al. The german naturalistic cycling study-comparing cycling speed of riders of different e-bikes and conventional bicycles[J]. Safety Science, 2017, 92:290-297. doi: 10.1016/j.ssci.2015.07.027
[21]	JIANG Kang, YANG Zhiwei, FENG Zhongxiang, et al. Effects of using mobile phones while cycling: a study from the perspectives of manipulation and visual strategies[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 83:291-303. doi: 10.1016/j.trf.2021.10.010
[22]	BAI Lu, SZE N N. Red light running behavior of bicyclists in urban area: effects of bicycle type and bicycle group size[J]. Travel Behaviour and Society, 2020, 21:226-234. doi: 10.1016/j.tbs.2020.07.003
[23]	WANG Chen, XU Chengcheng, XIA Jingxin, et al. The effects of safety knowledge and psychological factors on self-reported risky driving behaviors including group violations for e-bike riders in China[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 56: 344-353. doi: 10.1016/j.trf.2018.05.004
[24]	FISCHER C M, SANCHEZ C E, PITTMAN M, et al. Prevalence of bicycle helmet use by users of public bikeshare programs[J]. Annals of Emergency Medicine, 2012, 60(2):228-231. doi: 10.1016/j.annemergmed.2012.03.018 pmid: 22542733
[25]	GOODMAN A, GREEN J, WOODCOCK J. The role of bicycle sharing systems in normalising the image of cycling: an observational study of London cyclists[J]. Journal of Transport and Health, 2014, 1(1):5-8. doi: 10.1016/j.jth.2013.07.001
[26]	WU Xiaolin, XIAO Wangxin, DENG Conghui, et al. Unsafe riding behaviors of shared-bicycle riders in urban China: a retrospective survey[J]. Accident Analysis and Prevention, 2019, 131:1-7. doi: 10.1016/j.aap.2019.06.002
[27]	QIN Hua, WEI Yuhao, ZHANG Qidi, et al. An observational study on the risk behaviors of electric bicycle riders performing meal delivery at urban intersections in China[J]. Transportation Research Part F-Traffic Psychology and Behaviour, 2021, 79:107-117. doi: 10.1016/j.trf.2021.04.010
[28]	OVIEDO-TRESPALACIOS O, RUBIE E, HAWORTH N. Risky business: comparing the riding behaviours of food delivery and private bicycle riders[J]. Accident Analysis and Prevention, 2022, 177:DOI:10.1016/j.aap.2022.106820.
[29]	TWADDLE H, SCHENDZIELORZ T, FAKLER O. Bicycles in urban areas review of existing methods for modeling behavior[J]. Transportation Research Record, 2014, 2434:140-146. doi: 10.3141/2434-17
[30]	MOHAMMED H, BIGAZZI A Y, SAYED T. Characterization of bicycle following and overtaking maneuvers on cycling paths[J]. Transportation Research Part C: Emerging Technologies, 2019, 98:139-151. doi: 10.1016/j.trc.2018.11.012
[31]	刘启远, 孙剑, 田野, 等. 机非交互路段非机动车越线超车行为建模与仿真[J]. 同济大学学报:自然科学版, 2019, 47(11):1585-1592,1 599.
	LIU Qiyuan, SUN Jian, TIAN Ye, et al. Modeling and simulation of cross-line overtaking behavior of non-motorized vehicles at mixed flow road section[J]. Journal of Tongji University:Natural Science, 2019, 47(11):1585-1592,1 599.
[32]	LIU Zhenyuan, ZHONG Naiting, CHEN Junyi, et al. A modeling method for two-dimensional two-wheeler driving behavior during severe conflict interaction at intersections[J]. Accident Analysis and Prevention, 2024, 205:DOI:10.1016/j.aap.2024.107668.
[33]	GOLDHAMMER M, KOHLER S, ZERNETSCH S, et al. Intentions of vulnerable road users-detection and forecasting by means of machine Llearning[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(7):3035-3045. doi: 10.1109/TITS.6979
[34]	LI Jianqiang, NI Ying, SUN Jian. A two-layer integrated model for cyclist trajectory prediction considering multiple interactions with the environment[J]. Transportation Research Part C: Emerging Technologies, 2023, 155:DOI:10.1016/j.trc.2023.104304.
[35]	VLAKVELD W, VAN D K S, HAGENZIEKER M P. Cyclists' intentions to yield for automated cars at intersections when they have right of way: results of an experiment using high-quality video animations[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2020, 71:288-307. doi: 10.1016/j.trf.2020.04.012
[36]	HAMMAMI A, BORSOS A. Safety of cyclists interacting with autonomous vehicles: a combined microscopic simulation and SSAM analysis[J]. Travel Behaviour and Society, 2024, 36:DOI:10.1016/j.tbs.2024.100805.
[37]	BARNETT J, GIZINSKI N, MONDRAGON-PARRA E, et al. Automated vehicles sharing the road: surveying detection and localization of pedalcyclists[J]. IEEE Transactions on Intelligent Vehicles, 2021, 6(4):649-664. doi: 10.1109/TIV.2020.3046859
[38]	马社强, 张键源, 陈发城, 等. 非机动车闯红灯场景下辅助驾驶汽车驾驶人接管行为研究[J]. 北京理工大学学报, 2025, 45(5):493-502.
	MA Sheqiang, ZHANG Jianyuan, CHEN Facheng, et al. Driver takeover behavior of advanced driver assistance vehicles under scenarios of red light running by non-motorized vehicles[J]. Transactions of Beijing Institute of Technology, 2025, 45(5):493-502.
[39]	LI Xiaomeng, AFGHARI A P, OVIEDO-TRESPALACIOS O, et al. Cyclists perception and self-reported behaviour towards interacting with fully automated vehicles[J]. Transportation Research Part A: Policy and Practice, 2023, 173:DOI:10.1016/j.tra.2023.103713.
[40]	BERGE S H, DE W J, DODOU D, et al. Understanding cyclists' perception of driverless vehicles through eye-tracking and interviews[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2025, 109:399-420. doi: 10.1016/j.trf.2024.11.015
[41]	HU Lin, HU Xinting, WANG Jie, et al. Casualty risk of e-bike rider struck by passenger vehicle using China in-depth accident data[J]. Traffic Injury Prevention, 2020, 21(4):283-287. doi: 10.1080/15389588.2020.1747614 pmid: 32297809
[42]	廖静倩, 张道文, 高立, 等. 基于NAIS事故数据聚类的丁字路口危险场景研究[J]. 汽车安全与节能学报, 2021, 12(3):336-345.
	LIAO Jingqian, ZHANG Daowen, GAO Li, et al. Research on dangerous scene of T-intersection based on NAIS database accident data clustering[J]. Journal of Automotive Safety and Energy, 2021, 12(3):336-345.
[43]	周华, 刘峰, 李平飞, 等. 电动二轮车侧倾角对骑车人头部损伤影响研究[J]. 中国安全科学学报, 2019, 29(5):79-84. doi: 10.16265/j.cnki.issn1003-3033.2019.05.014
	ZHOU Hua, LIU Feng, LI Pingfei, et al. Impact of electric bicycles' roll angle on cyclist's head injury[J]. China Safety Science Journal, 2019, 29(5):79-84. doi: 10.16265/j.cnki.issn1003-3033.2019.05.014
[44]	王鑫, 张道文, 冉启林, 等. 电动自行车骑车人与汽车碰撞的动力学响应研究[J]. 中国安全科学学报, 2016, 26(6):152-156.
	WANG Xin, ZHANG Daowen, RAN Qilin, et al. Cyclists' dynamics response to car-electric bicycle crash[J]. China Safety Science Journal, 2016, 26(6):152-156.
[45]	FYHRI A, JOHANSSON O, BJORNSKAU T. Gender differences in accident risk with e-bikes-survey data from Norway[J]. Accident Analysis and Prevention, 2019, 132:DOI:10.1016/j.aap.2019.07.024.
[46]	TANG Tianpei, GUO Yuntao, ZHOU Xizhao, et al. Understanding electric bike riders' intention to violate traffic rules and accident proneness in China[J]. Travel Behaviour and Society, 2021, 23:25-38. doi: 10.1016/j.tbs.2020.10.010
[47]	HUANG Feihui. Exploring the factors influencing e-bike road safety: a survey study based on the experiences of Taiwanese cyclists[J]. International Journal of Industrial Ergonomics, 2022, 89:DOI:10.1016/j.ergon.2022.103292.
[48]	MORRISON C N, THOMPSON J, KONDO M C, et al. On-road bicycle lane types, roadway characteristics, and risks for bicycle crashes[J]. Accident Analysis and Prevention, 2019, 123:123-131. doi: 10.1016/j.aap.2018.11.017
[49]	王菁, 董春娇, 李鹏辉, 等. 考虑建成环境的电动自行车事故严重程度致因分析[J]. 交通运输系统工程与信息, 2024, 24(1):179-187.
	WANG Jing, DONG Chunjiao, LI Penghui, et al. Causal analysis of e-bike traffic accident severity considering built environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1):179-187.
[50]	WANG Tao, CHEN Jun, WANG Chen, et al. Understand e-bicyclist safety in China: crash severity modeling using a generalized ordered logit model[J]. Advances in Mechanical Engineering, 2018, 10(6):DOI:10.1177/1687814018781625.
[51]	林庆丰, 邓院昌, 胡继华. 机非交通事故中驾驶员过错和事故严重程度影响因素的Logistic回归分析[J]. 安全与环境工程, 2019, 26(5):187-193.
	LIN Qingfeng, DENG Yuanchang, HU Jihua. Logistic regression analysis of driver's fault and accident severity in motor- bicycle accidents[J]. Safety and Environmental Engineering, 2019, 26(5):187-193.
[52]	KLASSEN J, EL-BASYOUNY K, ISLAM M T. Analyzing the severity of bicycle-motor vehicle collision using spatial mixed logit models: a city of Edmonton case study[J]. Safety Science, 2014, 62:295-304. doi: 10.1016/j.ssci.2013.09.007
[53]	BEHNOOD A, MANNERING F. Determinants of bicyclist injury severities in bicycle-vehicle crashes: a random parameters approach with heterogeneity in means and variances[J]. Analytic Methods in Accident Research, 2017, 16:35-47. doi: 10.1016/j.amar.2017.08.001
[54]	ALNAWMASI N, ALI Y, YASMIN S. Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes[J]. Accident Analysis and Prevention, 2024, 194:DOI:10.1016/j.aap.2023.107339.
[55]	CHANG Fangrong, HAQUE M M, YASMIN S, et al. Crash injury severity analysis of e-bike riders: a random parameters generalized ordered probit model with heterogeneity in means[J]. Safety Science, 2022, 146:DOI:10.1016/j.ssci.2021.105545.
[56]	WANG Zhengwu, HUANG Shuai, WANG Jie, et al. Risk factors affecting crash injury severity for different groups of e-bike riders: a classification tree-based logistic regression model[J]. Journal of Safety Research, 2021, 76:176-183. doi: 10.1016/j.jsr.2020.12.009 pmid: 33653549
[57]	YANG Zaili, YANG Zhisen, SMITH J, et al. Risk analysis of bicycle accidents: a Bayesian approach[J]. Reliability Engineering and System Safety, 2021, 209:DOI:10.1016/j.ress.2021.107460.
[58]	SCARANO A, RICCARDI M R, MAURIELLO F, et al. Injury severity prediction of cyclist crashes using random forests and random parameters logit models[J]. Accident Analysis and Prevention, 2023, 192:DOI:10.1016/j.aap.2023.107275.
[59]	魏雯, 杜雨萌, 董傲然, 等. 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[J]. 交通信息与安全, 2022, 40(2):45-52, 62.
	WEI Wen, DU Yumeng, DONG Aoran, et al. An analysis of factors affecting injury of electric two-wheeler riders based on CIDAS data and ensemble learning[J]. Journal of Transport Information and Safety, 2022, 40(2):45-52, 62.
[60]	DING Hongliang, WANG Ruiqi, CHEN Tiantian, et al. A hybrid approach for modeling bicycle crash frequencies: Integrating random forest based SHAP model with random parameter negative binomial regression model[J]. Accident Analysis and Prevention, 2024, 208:DOI:10.1016/j.aap.2024.107778.
[61]	KIRCHER K, AHLSTROM C. Truck drivers' interaction with cyclists in right-turn situations[J]. Accident Analysis and Prevention, 2020, 142:DOI:10.1016/j.aap.2020.105515.
[62]	沈金星, 赵聪. 路侧直线式公交站台对非机动车行驶安全的影响[J]. 安全与环境学报, 2023, 23(6):1908-1915.
	SHEN Jinxing, ZHAO Cong. Influence of curbside bus stops on the driving safety of non-motorized vehicles[J]. Journal of Safety and Environment, 2023, 23(6):1908-1915.
[63]	CHEN Jingxu, LI Zhibin, WANG Wei, et al. Evaluating bicycle-vehicle conflicts and delays on urban streets with bike lane and on-street parking[J]. Transportation Letters: The International Journal of Transportation Research, 2018, 10(1):1-11.
[64]	BEITEL D, STIPANCIC J, MANAUGH K, et al. Assessing safety of shared space using cyclist-pedestrian interactions and automated video conflict analysis[J]. Transportation Research Part D: Transport and Environment, 2018, 65:710-724. doi: 10.1016/j.trd.2018.10.001
[65]	LIANG Xinyu, MENG Xianghai, ZHENG Lai. Investigating conflict behaviours and characteristics in shared space for pedestrians, conventional bicycles and e-bikes[J]. Accident Analysis and Prevention, 2021,158:DOI:10.1016/j.aap.2021.106167.
[66]	VAN HAPEREN W, DANIELS S, CEUNYNCK D T, et al. Yielding behavior and traffic conflicts at cyclist crossing facilities on channelized right-turn lanes[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 55:272-281. doi: 10.1016/j.trf.2018.03.012
[67]	王耀东, 陈雨人. 考虑驾驶员选择通过行为的机非严重冲突判别[J]. 交通信息与安全, 2015, 33(4):61-68.
	WANG Yaodong, CHEN Yuren. A method of identifying serious conflicts of motor and non-motor vehicles during passing maneuvers[J]. Journal of Transport Information and Safety, 2015, 33(4):61-68.
[68]	高志军, 马路, 闫学东. 城市交叉口非机动车交通冲突及其严重程度模型[J]. 中国安全科学学报, 2017, 27(3):31-36. doi: 10.16265/j.cnki.issn1003-3033.2017.03.006
	GAO Zhijun, MA Lu, YAN Xuedong. Research on models for traffic conflicts involving non-motor vehicles at city intersection and their severity[J]. China Safety Science Journal, 2017, 27(3):31-36. doi: 10.16265/j.cnki.issn1003-3033.2017.03.006
[69]	LIU Jianrong, CHEN Xinyu. Analysis of pedestrian-two-wheeler conflicts at green light digital countdown signals: a random parameter ordered logit model approach[J]. Accident Analysis and Prevention, 2024, 206:DOI:10.1016/j.aap.2024.107716.
[70]	龙科军, 张燕, 邹志云, 等. 基于车辆轨迹的信号交叉口机非冲突判别[J]. 交通运输系统工程与信息, 2021, 21(1):69-74.
	LONG Kejun, ZHANG Yan, ZOU Zhiyun, et al. Vehicle and non-motorized vehicle traffic conflict recognition at signalized intersection based on vehicle trajectory[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(1):69-74.
[71]	CHAI Hao, ZHANG Zhipeng, HU Hao, et al. Trajectory-based conflict investigations involving two-wheelers and cars at non-signalized intersections with computer vision[J]. Expert Systems with Applications, 2023, 230:DOI:10.1016/j.eswa.2023.120590.
[72]	XU Zheng, ZHENG Nan, LOGAN D B, et al. Assessing bicycle-vehicle conflicts at urban intersections utilizing a VR integrated simulation approach[J]. Accident Analysis and Prevention, 2023, 191:DOI:10.1016/j.aap.2023.107194.
[73]	BAI Lu, CHAN Chingyao, LIU Pan, et al. Identifying factors affecting the safety of mid-block bicycle lanes considering mixed 2-wheeled traffic flow[J]. Traffic Injury Prevention, 2017, 18(7):761-766. doi: 10.1080/15389588.2017.1303681 pmid: 28326809
[74]	KONG Dexue, ZHANG Cunbao, CHEN Feng, et al. Evaluating e-bike safety at unsignalized roundabouts using a Bayesian mixed logit model[J]. Accident Analysis and Prevention, 2025, 215:DOI:10.1016/j.aap.2025.108004.
[75]	宋卓康. 基于机非冲突的信号交叉口交通安全评价[D]. 长春: 吉林大学, 2023.
	SONG Zhuokang. Traffic safety evaluation at signalized intersections based on conflict between motor vehicles and non-motor vehicles[D]. Changchun: Jilin University, 2023.
[76]	马社强, 陆育霄, 董春娇, 等. 电动自行车违规行为严重程度分析与预测[J]. 中国安全科学学报, 2024, 34(11):172-178. doi: 10.16265/j.cnki.issn1003-3033.2024.11.0166
	MA Sheqiang, LU Yuxiao, DONG Chunjiao, et al. Severity analyses and prediction of e-bikes violated behaviors[J]. China Safety Science Journal, 2024, 34(11):172-178. doi: 10.16265/j.cnki.issn1003-3033.2024.11.0166
[77]	巫诚诚, 陈大伟. 交叉口非机动车冲突易发点空间预测模型[J]. 中国安全科学学报, 2021, 31(8):165-171. doi: 10.16265/j.cnki.issn1003-3033.2021.08.023
	WU Chengcheng, CHEN Dawei. Spatial prediction model for risk points of non-motor vehicle conflict in intersections[J]. China Safety Science Journal, 2021, 31(8):165-171. doi: 10.16265/j.cnki.issn1003-3033.2021.08.023
[78]	任丽丽, 吴江玲, 郭旭亮, 等. 无信号环形交叉口机非冲突机器学习预测方法[J]. 科学技术与工程, 2023, 23(31):13592-13 600.
	REN Lili, WU Jiangling, GUO Xuliang, et al. Traffic conflict prediction of motorized and non-motorized vehicles at unsignalized round about using machine learning[J]. Science Technology and Engineering, 2023, 23(31):13592-13 600.
[79]	ALNAWMASI N, ALOGAILI A, RANGASWAMY R, et al. A temporal statistical assessment of the effectiveness of bicyclist safety helmets in mitigating injury severities in vehicle/bicyclist crashes[J]. Analytic Methods in Accident Research, 2024, 43:DOI:10.1016/j.amar.2024.100338.
[80]	WOOD J M, BLACK A A, TYRRELL R A. Increasing the conspicuity of cyclists at night by using bicycle lights and clothing to highlight their biological motion to oncoming drivers[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 90:326-332. doi: 10.1016/j.trf.2022.09.005
[81]	TRUONG L T, NGUYEN H T T, DE GRUYTER C. Mobile phone use among motorcyclists and electric bike riders: a case study of Hanoi, Vietnam[J]. Accident Analysis and Prevention, 2016, 91:208-215. doi: 10.1016/j.aap.2016.03.007
[82]	LYU Huitao, LI Haojie, SZE N N, et al. The impacts of non-motorized traffic enforcement cameras on red light violations of cyclists at signalized intersections[J]. Journal of Safety Research, 2022, 83:310-322. doi: 10.1016/j.jsr.2022.09.005 pmid: 36481022
[83]	YAN Xinping, MA Ming, HUANG Helai, et al. Motor vehicle-bicycle crashes in Beijing: irregular maneuvers, crash patterns, and injury severity[J]. Accident Analysis and Prevention, 2011, 43(5):1751-1758. doi: 10.1016/j.aap.2011.04.006
[84]	MADSEN T K O, LAHRMANN H. Comparison of five bicycle facility designs in signalized intersections using traffic conflict studies[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2017, 46:438-450. doi: 10.1016/j.trf.2016.05.008
[85]	张骏, 郑楠, 黄崇轩. 城市中心区非机动车系统设计优化与探索[J]. 北京航空航天大学学报, 2019, 45(6):1218-1231.
	ZHANG Jun, ZHENG Nan, HUANG Chongxuan. Optimal-design and exploration for non-motor vehicle system in urban center[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(6):1218-1231.
[86]	宋浪, 王健, 杨滨毓, 等. 连续流交叉口左转非机动车交通组织创新设计[J]. 交通运输系统工程与信息, 2022, 22(1):225-233,242.
	SONG Lang, WANG Jian, YANG Binyu, et al. Innovative design of transportation organization for left-turn non-motor vehicles at continuous flow intersection[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1):225-233,242.
[87]	李英帅, 马泽超, 王雯婧, 等. 考虑非机动车影响的直线式单泊位公交停靠站设置优化[J]. 交通信息与安全, 2021, 39(5):137-143.
	LI Yingshuai, MA Zechao, WANG Wenjing, et al. Optimization of single-berth curbside bus stops considering impacts of non-motorized vehicles[J]. Journal of Transport Information and Safety, 2021, 39(5):137-143.
[88]	ZHANG Chenxiao, MA Yongfeng, SAYED T, et al. Exploring the impact of right-turn safety measures on e-bike-heavy vehicle conflicts at signalized intersections[J]. Accident Analysis and Prevention, 2024, 206:DOI:10.1016/j.aap.2024.107722.

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)