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dc.contributor.authorRankovic, Vesna-
dc.contributor.authorĐonić, Andrija-
dc.contributor.authorGeroski, Tijana-
dc.contributor.editorGlišović, Jasna-
dc.contributor.editorGrujic, Ivan-
dc.date.accessioned2024-11-21T11:39:59Z-
dc.date.available2024-11-21T11:39:59Z-
dc.date.issued2024-
dc.identifier.isbn978-86-6335-120-2en_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/21579-
dc.description.abstractRoad traffic accidents are identified as a significant societal issue based on extensive and comprehensive research in public health and traffic safety. Such incidents lead to significant negative outcomes, including human casualties, economic consequences, vehicle damage, and significant medical costs. Developing predictive models is crucial for identifying risk factors associated with accidents, thereby improving understanding of accident causes and enabling more effective prevention interventions. The occurrence of traffic accidents is influenced by a multitude of factors, including driver behavior, vehicle characteristics, weather conditions, road volume, road geometry, type of road, road conditions, speed limits, frequency of police controls, etc. In this paper, machine learning (ML) techniques are used to develop the traffic accident prediction model due to the non-linear relationship between input and output variables. The research investigates the influence of certain input variables on the number of traffic accidents, as their optimal choice significantly affects the prediction performance. The random forest, support vector machine, and neural networks are employed for data preprocessing and model development. Statistical indicators are used to evaluate the performance of the developed models. Based on the obtained performances, the developed ML models accurately predict the number of traffic accidents.en_US
dc.language.isoenen_US
dc.publisherFaculty of Engineering, University of Kragujevac Sestre Janjić 6, 34000 Kragujevac, Serbiaen_US
dc.subjectroad safetyen_US
dc.subjectroad accidenten_US
dc.subjectpredictionen_US
dc.subjectmachine learningen_US
dc.titleROAD TRAFFIC ACCIDENTS PREDICTION USING MACHINE LEARNING METHODSen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisheden_US
dc.type.versionPublishedVersionen_US
dc.source.conference10th International Congress Motor Vehicles & Motors 2024en_US
Налази се у колекцијама:Faculty of Engineering, Kragujevac

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