GIS-based assessment of pedestrian-vehicle accidents in terms of safety with four different ML models

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dc.authoridKatanalp, Burak Yiğit/0000-0002-7172-8192
dc.authorscopusid57217418656
dc.authorscopusid57211549816
dc.authorwosidKatanalp, Burak Yiğit/AAM-1366-2021
dc.contributor.authorKatanalp, Burak Yigit
dc.contributor.authorEren, Ezgi
dc.date.accessioned2023-01-12T20:00:04Z
dc.date.available2023-01-12T20:00:04Z
dc.date.issued2022
dc.departmentN/A/Departmenten_US
dc.description.abstractIn this study, both micro and macro level evaluation of pedestrian-vehicle crashes were conducted. Macro-level findings were obtained with GIS-based density analyzes, and critical road segments were determined. The data on road characteristics, traffic characteristics, built environment and land use were collected in 70 critical urban road segments. While conducting micro-level research, commonly used multilayer perceptron and C4.5 decision tree, as well as innovative converted fuzzy-decision model and revised fuzzy-decision model, which significantly reduces the expert judgements on fuzzy models, were used. Significant rules were extracted, and were evaluated from safety perspective. Information gain ratio was used to deal with the black-box structure of machine learning models and to examine independent factors in-depth. The best performance was achieved in revised fuzzy decision model with 68.57% accuracy. The results revealed that land use, parking and peak hour volume have high effect, as well as public transport, speed and road type factors have the greatest effect on pedestrian safety. In the light of the results, various managerial implications such as controlling the density of public transport on main arterials, preventing stop-and-go effects, and monitoring vehicle speeds especially during peak hours were suggested to improve pedestrian safety.en_US
dc.identifier.doi10.1080/19439962.2021.1978022
dc.identifier.endpage1632en_US
dc.identifier.issn1943-9962
dc.identifier.issn1943-9970
dc.identifier.issn1943-9962en_US
dc.identifier.issn1943-9970en_US
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85115057835en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1598en_US
dc.identifier.urihttps://doi.org/10.1080/19439962.2021.1978022
dc.identifier.urihttps://hdl.handle.net/11454/77281
dc.identifier.volume14en_US
dc.identifier.wosWOS:000696269900001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofJournal Of Transportation Safety & Securityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPedestrian safetyen_US
dc.subjectgeographical information systemen_US
dc.subjectmachine learningen_US
dc.subjectfuzzy logicen_US
dc.subjectaccident analysisen_US
dc.subjectRoad Traffic Accidentsen_US
dc.subjectCrash Injury Severityen_US
dc.subjectBuilt Environmenten_US
dc.subjectSpatial-Analysisen_US
dc.subjectDecision Rulesen_US
dc.subjectRisk-Factorsen_US
dc.subjectNew-Yorken_US
dc.subjectMembership Functionsen_US
dc.subjectDensity-Estimationen_US
dc.subjectLogit Modelen_US
dc.titleGIS-based assessment of pedestrian-vehicle accidents in terms of safety with four different ML modelsen_US
dc.typeArticleen_US

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