A Fuzzy Clustering Approach to Identify Pedestrians’ Traffic Behavior Patterns

IF 1.4 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Journal of research in health sciences Pub Date : 2023-09-29 DOI:10.34172/jrhs.2023.127
Parisa Saeipour, Parvin Sarbakhsh, Saman Salemi, Fatemeh Bakhtari Aghdam
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Abstract

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.
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行人交通行为模式识别的模糊聚类方法
背景:行人交通行为的模式识别可以通过更具体的调查提高兴趣群体的管理效率,并促进规划。本研究旨在通过模糊聚类算法对行人的交通行为模式进行评价,并对行人高危交通行为的相关因素进行评估。研究设计:本研究是基于横断面研究数据的二次方法学研究。方法:采用模糊c均值(FCM)作为机器学习聚类方法,通过“行人行为问卷”(PBQ)对伊朗乌尔米娅地区600名行人进行数据采集,利用PBQ的5个域进行交通行为模式识别。采用多元logistic回归方法对交通行为危险因素进行识别。结果:结果显示低危行为和高危行为两类。64.33%的行人属于低风险聚集类;年龄≤33岁(OR= 1.92, P=0.001)、受教育年限≤6年(OR=1.74, P=0.010)、男性(OR=1.90, P=0.001)、未婚行人(OR=3.61, P=0.007)和公共交通使用者(OR=2.01, P=0.002)发生高危交通行为的可能性较大。结论:通过FCM识别出Urmia行人的低风险和高风险交通行为模式。这项研究的结果将有助于政策制定者推广安全措施和训练行人。
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来源期刊
Journal of research in health sciences
Journal of research in health sciences PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
2.30
自引率
13.30%
发文量
7
期刊介绍: The Journal of Research in Health Sciences (JRHS) is the official journal of the School of Public Health; Hamadan University of Medical Sciences, which is published quarterly. Since 2017, JRHS is published electronically. JRHS is a peer-reviewed, scientific publication which is produced quarterly and is a multidisciplinary journal in the field of public health, publishing contributions from Epidemiology, Biostatistics, Public Health, Occupational Health, Environmental Health, Health Education, and Preventive and Social Medicine. We do not publish clinical trials, nursing studies, animal studies, qualitative studies, nutritional studies, health insurance, and hospital management. In addition, we do not publish the results of laboratory and chemical studies in the field of ergonomics, occupational health, and environmental health
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