{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T11:14:24Z","timestamp":1792149264517,"version":"4.4.0"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T00:00:00Z","timestamp":1676678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["41971421"],"award-info":[{"award-number":["41971421"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["41601495"],"award-info":[{"award-number":["41601495"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["2021RC3099"],"award-info":[{"award-number":["2021RC3099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["2022JJ30590"],"award-info":[{"award-number":["2022JJ30590"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"science and technology innovation Program of Hunan","award":["41971421"],"award-info":[{"award-number":["41971421"]}]},{"name":"science and technology innovation Program of Hunan","award":["41601495"],"award-info":[{"award-number":["41601495"]}]},{"name":"science and technology innovation Program of Hunan","award":["2021RC3099"],"award-info":[{"award-number":["2021RC3099"]}]},{"name":"science and technology innovation Program of Hunan","award":["2022JJ30590"],"award-info":[{"award-number":["2022JJ30590"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["41971421"],"award-info":[{"award-number":["41971421"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["41601495"],"award-info":[{"award-number":["41601495"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2021RC3099"],"award-info":[{"award-number":["2021RC3099"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2022JJ30590"],"award-info":[{"award-number":["2022JJ30590"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geospatial analysis methods have been successfully adopted for traffic accident analysis. As characterized by high driving speeds, diverse vehicle types, and isolated traffic environments, expressways are confronted with more serious accident risks than urban roads. In this paper, we propose a combined method based on improved density clustering and the Bayesian inference network to explore spatiotemporal patterns of expressway accidents. Firstly, the spatiotemporal accident neighborhood is integrated into the DBSCAN clustering algorithm to discover multi-scale expressway black spots. Secondly, the Bayesian network model is separately employed in both local-scale black spots and regional-scale expressway networks to fully explore spatially heterogenous accident factors in various black spots and expressways. The experimental results show that the proposed method can correctly extract spatiotemporal aggregation patterns of multi-scale expressway black spots and meanwhile efficiently discover diverse causal factors for various black spots and expressways, providing a comprehensive analysis of accident prevention and safety management.<\/jats:p>","DOI":"10.3390\/ijgi12020073","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T05:57:52Z","timestamp":1676872672000},"page":"73","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Exploring Spatiotemporal Patterns of Expressway Traffic Accidents Based on Density Clustering and Bayesian Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0227-4940","authenticated-orcid":false,"given":"Yunfei","family":"Zhang","sequence":"first","affiliation":[{"name":"Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangqi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9562-0138","authenticated-orcid":false,"given":"Qiuping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehang","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajun","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hazaymeh, K., Almagbile, A., and Alomari, A.H. 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