A Fuzzy Clustering Approach to Identify Pedestrians' Traffic Behavior Patterns.
Background: Pattern recognition of pedestrians' traffic behavior can enhance the management efficiency of interested groups by targeting access to them and facilitating planning via more specific surveys. This study aimed to evaluate the pedestrians' traffic behavior pattern by fuzzy clustering algo...
| Publicado en: | Journal of Research in Health Sciences Vol. 23; no. 3; pp. 1 - 10 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Hamadan University of Medical Sciences, School of Public Health
Summer2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173369716&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173369716 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22287795 903Q jtl: Journal of Research in Health Sciences issn: 22287795 maglogo: N pubinfo: dt: Summer2023 vid: 23 iid: 3 pid: 54266 pub: Hamadan University of Medical Sciences, School of Public Health artinfo: ui: 173369716 173369716 173369716 10.34172/jrhs.2023.127 173369716 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A Fuzzy Clustering Approach to Identify Pedestrians' Traffic Behavior Patterns. aug: au: Saeipour, Parisa Sarbakhsh, Parvin Salemi, Saman Aghdam, Fatemeh Bakhtari affil: Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran sug: subj: Accidents, Traffic Prevention and Control Pedestrians Psychosocial Factors Risk Taking Behavior Safety Algorithms Public Health Cluster Analysis Surveys Cross Sectional Studies Machine Learning Questionnaires Multiple Logistic Regression Odds Ratio Confidence Intervals Policy Making Data Analysis Software Human Male Female Adolescence Young Adult Adult Middle Age Aged Aged, 80 and Over Descriptive Statistics Funding Source Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: Pattern recognition of pedestrians' traffic behavior can enhance the management efficiency of interested groups by targeting access to them and facilitating planning via more specific surveys. This study aimed to evaluate the pedestrians' traffic behavior pattern by fuzzy clustering algorithm and assess the factors related to higher-risk traffic behavior of pedestrians. Study Design: This study is a secondary methodological study based on the data from a cross-sectional study. Methods: The fuzzy c-means (FCM), as a machine learning clustering method, was conducted to identify the pattern of traffic behaviors by collecting data from 600 pedestrians in Urmia, Iran via "the Pedestrian Behavior Questionnaire" (PBQ) and using 5 domains of PBQ. Multiple logistic regression was fitted to identify risk factors of traffic behaviors. Results: Results revealed two clusters consisting of lower-risk and higher-risk behaviors. The majority of pedestrians (64.33%) were in the lower-risk cluster. Subjects ≤ 33 years old (Odds ratio [OR] = 1.92, P < 0.001), subjects with ≤ 6 years of education (OR = 1.74, P = 0.010), males (OR = 1.90, P = 0.001), unmarried pedestrians (OR = 3.61, P = 0.007), and users of public transportation (OR = 2.01, P = 0.002) were more likely to have higher-risk traffic behavior. Conclusion: We identified traffic behavior patterns of Urmia pedestrians with lower-risk and higher-risk behaviors via FCM. The findings from this study would be helpful for policymakers to promote safety measures and train pedestrians. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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