{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T03:01:35Z","timestamp":1781578895630,"version":"3.54.5"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:00:00Z","timestamp":1715644800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100020369","name":"Instituto Nacional de Ci\u00eancia e Tecnologia em \u00c1reas Umidas","doi-asserted-by":"publisher","award":["202248"],"award-info":[{"award-number":["202248"]}],"id":[{"id":"10.13039\/100020369","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019936","name":"Key Laboratory of Heavy Rain and Drought-Flood Disasters in Plateau and Basins of Sichuan Province","doi-asserted-by":"publisher","award":["QCCK2021-011"],"award-info":[{"award-number":["QCCK2021-011"]}],"id":[{"id":"10.13039\/501100019936","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Bicycle safety has emerged as a pressing concern within the vulnerable transportation community. Numerous studies have been conducted to identify the significant factors that contribute to the severity of cyclist injuries, yet the findings have been subject to uncertainty due to unobserved heterogeneity and class imbalance. This research aims to address these issues by developing a model to examine the impact of key factors on cyclist injury severity, accounting for data heterogeneity and imbalance. To incorporate unobserved heterogeneity, a total of 3,895 bicycle accidents were categorized into three homogeneous sub-accident clusters using Latent Class Cluster Analysis (LCA). Additionally, five over-sampling techniques were employed to mitigate the effects of data imbalance in each accident cluster category. Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. The optimal BN models for each accident cluster type provided insights into the key factors associated with cyclist injury severity. The results indicate that the key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters. Female cyclists, adverse weather conditions such as rain and snow, and off-peak periods were identified as key factors in several subclasses of accident clusters. Conversely, factors such as the week of the accident, characteristics of the trafficway, the season, drivers failing to yield to the right-of-way, distracted cyclists, and years of driving experience were found to be key factors in only one subcluster of accident clusters. Additionally, factors such as the time of the crash, gender of the cyclist, and weather conditions exhibit varying levels of heterogeneity across different accident clusters, and in some cases, exhibit opposing effects.<\/jats:p>","DOI":"10.1371\/journal.pone.0301293","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T17:25:24Z","timestamp":1715707524000},"page":"e0301293","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance"],"prefix":"10.1371","volume":"19","author":[{"given":"Xinchi","family":"Dong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7139-8271","authenticated-orcid":true,"given":"Daowen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaojian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianshu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,5,14]]},"reference":[{"key":"pone.0301293.ref001","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jsr.2020.02.006","article-title":"Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates","volume":"73","author":"J Liu","year":"2020","journal-title":"Journal of Safety Research"},{"issue":"1","key":"pone.0301293.ref002","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.aap.2010.10.031","article-title":"Powered two wheelers in a changing world\u2014Challenges and opportunities","volume":"44","author":"N Haworth","year":"2012","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref003","doi-asserted-by":"crossref","first-page":"106616","DOI":"10.1016\/j.aap.2022.106616","article-title":"Bicyclists injury severities: An empirical assessment of temporal stability","volume":"168","author":"SH Hosseini","year":"2022","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref004","unstructured":"World Health Organization, 2020. Cyclist Safety: An Information Resource for Decision- Makers and Practitioners."},{"key":"pone.0301293.ref005","unstructured":"National Highway Traffic Safety Administration, 2020. Bicyclists and other cyclists: 2018 data. (Traffic Safety Facts. Report No. DOT HS 812 884). Washington, DC."},{"issue":"1","key":"pone.0301293.ref006","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1080\/01441647.2021.1915898","article-title":"Cycling behaviour in 17 countries across 6 continents: levels of cycling, who cycles, for what purpose, and how far?","volume":"42","author":"R Goel","year":"2021","journal-title":"Transport Reviews"},{"key":"pone.0301293.ref007","doi-asserted-by":"crossref","first-page":"105936","DOI":"10.1016\/j.aap.2020.105936","article-title":"Bayesian networks for imbalance data to investigate the contributing factors to fatal injury crashes on the Ghanaian highways","volume":"150","author":"M Yahaya","year":"2021","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref008","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.jsr.2020.12.008","article-title":"Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach","volume":"76","author":"L Song","year":"2021","journal-title":"Journal of Safety Research"},{"key":"pone.0301293.ref009","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.tbs.2022.09.003","article-title":"Large truck fatal crash severity segmentation and analysis incorporating all parties involved: A Bayesian network approach[J]","volume":"30","author":"J Wu","year":"2023","journal-title":"Travel Behaviour and Society"},{"issue":"5","key":"pone.0301293.ref010","doi-asserted-by":"crossref","first-page":"e0251492","DOI":"10.1371\/journal.pone.0251492","article-title":"Statistical modeling of numbers of human deaths per road traffic accident in the Oromia region, Ethiopia","volume":"16","author":"MA Aga","year":"2021","journal-title":"PLOS ONE"},{"key":"pone.0301293.ref011","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.trd.2013.02.001","article-title":"Comparative analysis of the safety effects of electric bikes at signalized intersections","volume":"20","author":"L Bai","year":"2013","journal-title":"Transportation Research Part D: Transport and Environment"},{"key":"pone.0301293.ref012","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.aap.2017.04.020","article-title":"The effect of crash characteristics on cyclist injuries: An analysis of Virginia automobile-bicycle crash data","volume":"104","author":"E Robartes","year":"2017","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref013","doi-asserted-by":"crossref","first-page":"105326","DOI":"10.1016\/j.aap.2019.105326","article-title":"A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates","volume":"134","author":"Q Hou","year":"2020","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref014","doi-asserted-by":"crossref","first-page":"106035","DOI":"10.1016\/j.aap.2021.106035","article-title":"Incorporating accident liability into crash risk analysis: A multidimensional risk source approach","volume":"153","author":"X Wang","year":"2021","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.amar.2013.09.001","article-title":"Analytic methods in accident research: Methodological frontier and future directions","volume":"1","author":"FL Mannering","year":"2014","journal-title":"Analytic Methods in Accident Research"},{"issue":"1","key":"pone.0301293.ref016","doi-asserted-by":"crossref","first-page":"e0263030","DOI":"10.1371\/journal.pone.0263030","article-title":"A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data","volume":"17","author":"X Fu","year":"2022","journal-title":"PLOS ONE"},{"key":"pone.0301293.ref017","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jsr.2019.04.008","article-title":"Applying machine learning approaches to analyze the vulnerable road-users\u2019 crashes at statewide traffic analysis zones","volume":"70","author":"MS Rahman","year":"2019","journal-title":"Journal of Safety Research"},{"key":"pone.0301293.ref018","doi-asserted-by":"crossref","first-page":"107460","DOI":"10.1016\/j.ress.2021.107460","article-title":"Risk analysis of bicycle accidents: A Bayesian approach","volume":"209","author":"Z Yang","year":"2021","journal-title":"Reliability Engineering & System Safety"},{"issue":"6","key":"pone.0301293.ref019","first-page":"1046","article-title":"Analysis of influencing factors of multi-vehicle accidents on mountainous highways based on SVM model","volume":"44","author":"GY Li","year":"2020","journal-title":"Journal of Wuhan University of Technology (Transportation Science and Engineering Edition)"},{"key":"pone.0301293.ref020","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.amar.2017.08.001","article-title":"Determinants of bicyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances","volume":"16","author":"A Behnood","year":"2017","journal-title":"Analytic Methods in Accident Research"},{"issue":"3","key":"pone.0301293.ref021","doi-asserted-by":"crossref","first-page":"e0281901","DOI":"10.1371\/journal.pone.0281901","article-title":"Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data","volume":"18","author":"Seyed Iman Mohammadpour","year":"2023","journal-title":"PLOS ONE"},{"key":"pone.0301293.ref022","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jsr.2020.11.012","article-title":"Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models","volume":"76","author":"Z Lin","year":"2021","journal-title":"Journal of Safety Research"},{"key":"pone.0301293.ref023","unstructured":"Crash Report Sampling System | NHTSA"},{"key":"pone.0301293.ref024","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jsr.2014.03.012","article-title":"Aggravating and mitigating factors associated with cyclist injury severity in Denmark","volume":"50","author":"S Kaplan","year":"2014","journal-title":"Journal of Safety Research"},{"issue":"1","key":"pone.0301293.ref025","doi-asserted-by":"crossref","first-page":"185","DOI":"10.3390\/su15010185","article-title":"Latent Class Cluster Analysis and Mixed Logit Model to Investigate Pedestrian Crash Injury Severity","volume":"15","author":"A Esmaili","year":"2023","journal-title":"Sustainability"},{"key":"pone.0301293.ref026","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.aap.2018.12.016","article-title":"Pedestrian crash analysis with latent class clustering method","volume":"124","author":"M Sun","year":"2019","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref027","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.jsr.2020.12.004","article-title":"Data mining approach to model bus crash severity in Australia","volume":"76","author":"SA Samerei","year":"2021","journal-title":"Journal of Safety Research"},{"key":"pone.0301293.ref028","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.aap.2018.01.035","article-title":"Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities","volume":"113","author":"EK Adanu","year":"2018","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref029","first-page":"1","article-title":"The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models","author":"C Tantithamthavorn","year":"2018","journal-title":"IEEE Transactions on Software Engineering"},{"issue":"16","key":"pone.0301293.ref030","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"pone.0301293.ref031","doi-asserted-by":"crossref","unstructured":"Cheng J, Bell D A, Liu W. Learning belief networks from data: An information theory based approach[C]\/\/Proceedings of the sixth international conference on Information and knowledge management. 1997: 325\u2013331.","DOI":"10.1145\/266714.266920"},{"key":"pone.0301293.ref032","first-page":"52","volume-title":"Theory Refinement on Bayesian Networks","author":"Wray Buntine","year":"1991"},{"issue":"4","key":"pone.0301293.ref033","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.ijresmar.2004.06.002","article-title":"Country and consumer segmentation: multi-level latent class analysis of financial product ownership","volume":"21","author":"THA Bijmolt","year":"2004","journal-title":"International Journal of Research in Marketing"},{"issue":"2","key":"pone.0301293.ref034","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.aap.2006.07.002","article-title":"Bicyclist injury severities in bicycle\u2014motor vehicle accidents","volume":"39","author":"JK Kim","year":"2007","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref035","first-page":"1","article-title":"Exploring injury severity of bicycle-motor vehicle crashes: A two-stage approach integrating latent class analysis and random parameter logit model","author":"Z Sun","year":"2021","journal-title":"Journal of Transportation Safety & Security"},{"key":"pone.0301293.ref036","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.jsr.2021.09.005","article-title":"Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle\u2014bicycle crashes","volume":"79","author":"SA Samerei","year":"2021","journal-title":"Journal of Safety Research"},{"key":"pone.0301293.ref037","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.aap.2018.08.009","article-title":"Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models","volume":"120","author":"Z Li","year":"2018","journal-title":"Accident Analysis & Prevention"},{"key":"pone.0301293.ref038","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.aap.2019.04.001","article-title":"Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes","volume":"129","author":"Z Li","year":"2019","journal-title":"Accident Analysis & Prevention"},{"issue":"1","key":"pone.0301293.ref039","doi-asserted-by":"crossref","first-page":"e0262683","DOI":"10.1371\/journal.pone.0262683","article-title":"Helmet wearing behavior where people often ride motorcycle in Ethiopia: A cross-sectional study","volume":"17","author":"D Bedru","year":"2022","journal-title":"PLOS ONE"},{"key":"pone.0301293.ref040","article-title":"Bicyclist injury severity classification using a random parameter logit model","author":"S Das","year":"2023","journal-title":"International Journal of Transportation Science and Technology"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0301293","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T17:25:59Z","timestamp":1715707559000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0301293"}},"subtitle":[],"editor":[{"given":"Quan","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,5,14]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0301293","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,14]]}}}