{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(1)"],"submitter":["Iranmanesh M"],"pubmed_abstract":["Traffic safety forecast models are mainly used to rank road segments. While existing studies have primarily focused on identifying segments in urban networks, rural networks have received less attention. However, rural networks seem to have a higher risk of severe crashes. This paper aims to analyse traffic crashes on rural roads to identify the influencing factors on the crash frequency and present a framework to develop a spatial-temporal crash risk map to prioritise high-risk segments on different days. The crash data of Khorasan Razavi province is used in this study. Crash frequency data with the temporal resolution of one day and spatial resolution of 1500 m from loop detectors are analysed. Four groups of influential factors, including traffic parameters (e.g. traffic flow, speed, ti"],"journal":["Scientific reports"],"pagination":["20024"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9681741"],"repository":["biostudies-literature"],"pubmed_title":["Identifying high crash risk segments in rural roads using ensemble decision tree-based models."],"pmcid":["PMC9681741"],"pubmed_authors":["Iranmanesh M","Seyedabrishami S","Moridpour S"],"additional_accession":[]},"is_claimable":false,"name":"Identifying high crash risk segments in rural roads using ensemble decision tree-based models.","description":"Traffic safety forecast models are mainly used to rank road segments. While existing studies have primarily focused on identifying segments in urban networks, rural networks have received less attention. However, rural networks seem to have a higher risk of severe crashes. This paper aims to analyse traffic crashes on rural roads to identify the influencing factors on the crash frequency and present a framework to develop a spatial-temporal crash risk map to prioritise high-risk segments on different days. The crash data of Khorasan Razavi province is used in this study. Crash frequency data with the temporal resolution of one day and spatial resolution of 1500 m from loop detectors are analysed. Four groups of influential factors, including traffic parameters (e.g. traffic flow, speed, ti","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-05T10:57:12.084Z","creation":"2025-04-05T10:57:12.084Z"},"accession":"S-EPMC9681741","cross_references":{"pubmed":["36414672"],"doi":["10.1038/s41598-022-24476-z"]}}