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dc.contributor.author박준영-
dc.date.accessioned2024-02-01T04:17:22Z-
dc.date.available2024-02-01T04:17:22Z-
dc.date.issued2024-01-
dc.identifier.citationELECTRONIC RESEARCH ARCHIVEen_US
dc.identifier.issn2688-1594en_US
dc.identifier.urihttps://www.aimspress.com/article/doi/10.3934/era.2024029en_US
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/188134-
dc.description.abstractThe factors that affect the severity of crashes must be identified for pedestrian and traffic safety in urban roads. Specifically, in the case of urban road crashes, these crashes occur due to the complex interaction of various factors. Therefore, it is necessary to collect high-quality data that can derive these various factors. Accordingly, this study collected crash data, which included detailed crash factor data on the huge urban and mid-level roads. Using this, various crash factors including driver, vehicle, road, environment, and crash characteristics are constructed to develop a crash severity prediction model. Through this, this study identified more detailed factors affecting the severity of urban road crashes. The crash severity model was developed using both machine learning and statistical models because the insights that can be obtained from the latest technology and traditional methods are different. Therefore, the binary logit model, a support vector machine, and extreme gradient boosting were developed using key variables derived from the multiple correspondence analysis and Boruta-SHapley Additive exPlanations. The main result of this study shows that the crash severity decreased at four-street intersections and when traffic segregation facilities were installed. The findings of this study can be used to establish a traffic safety anagement strategy to reduce the severity of crashes on urban roads.en_US
dc.description.sponsorshipThis research was supported by a grant (2021-MOIS38-001) of Proactive Technology Development on Safety Accident for Vulnerable Group and Facility funded by the Ministry of the Interior and Safety (MOIS, South Korea).en_US
dc.languageen_USen_US
dc.publisherAMER INST MATHEMATICAL SCIENCES-AIMSen_US
dc.relation.ispartofseries32(1);584-607-
dc.subjectcrash severity modelen_US
dc.subjectin-vehicle dashcam video dataen_US
dc.subjectcrash dataen_US
dc.subjecttraffic safetyen_US
dc.subjectmachine learningen_US
dc.subjecturban road traffic managementen_US
dc.titleAssessing crash severity of urban roads with data mining techniques using big data from in-vehicle dashcamen_US
dc.typeArticleen_US
dc.identifier.doi10.3934/era.2024029en_US
dc.relation.page584-607-
dc.relation.journalELECTRONIC RESEARCH ARCHIVE-
dc.contributor.googleauthorPark, Nuri-
dc.contributor.googleauthorCho, Junhan-
dc.contributor.googleauthorPark, Juneyoung-
dc.relation.code2024006211-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF ENGINEERING SCIENCES[E]-
dc.sector.departmentDEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING-
dc.identifier.pidjuneyoung-
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COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > TRANSPORTATION AND LOGISTICS ENGINEERING(교통·물류공학과) > Articles
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