{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:36:59Z","timestamp":1779907019988,"version":"3.53.1"},"reference-count":67,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science, Technological Development and Innovations of the Republic of Serbia","award":["451-03-47\/2023-01\/200051"],"award-info":[{"award-number":["451-03-47\/2023-01\/200051"]}]},{"name":"Ministry of Science, Technological Development and Innovations of the Republic of Serbia","award":["451-03-47\/2023-01\/200134"],"award-info":[{"award-number":["451-03-47\/2023-01\/200134"]}]},{"name":"Ministry of Science, Technological Development and Innovations of the Republic of Serbia","award":["451-03-47\/2023-01\/200017"],"award-info":[{"award-number":["451-03-47\/2023-01\/200017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Road traffic accidents are a significant public health issue, accounting for almost 1.3 million deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of subjective and objective factors contribute to the occurrence of traffic accidents, making it difficult to predict and prevent them on new road sections. Artificial neural networks (ANN) have demonstrated their effectiveness in predicting traffic accidents using limited data sets. This study presents two ANN models to predict traffic accidents on common roads in the Republic of Serbia and the Republic of Srpska (Bosnia and Herzegovina) using objective factors that can be easily determined, such as road length, terrain type, road width, average daily traffic volume, and speed limit. The models predict the number of traffic accidents, as well as the severity of their consequences, including fatalities, injuries and property damage. The developed optimal neural network models showed good generalization capabilities for the collected data foresee, and could be used to accurately predict the observed outputs, based on the input parameters. The highest values of r2 for developed models ANN1 and ANN2 were 0.986, 0.988, and 0.977, and 0.990, 0.969, and 0.990, accordingly, for training, testing and validation cycles. Identifying the most influential factors can assist in improving road safety and reducing the number of accidents. Overall, this research highlights the potential of ANN in predicting traffic accidents and supporting decision-making in transportation planning.<\/jats:p>","DOI":"10.3390\/a16050257","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T06:32:58Z","timestamp":1684391578000},"page":"257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Predicting Road Traffic Accidents\u2014Artificial Neural Network Approach"],"prefix":"10.3390","volume":"16","author":[{"given":"Dragan","family":"Gatari\u0107","sequence":"first","affiliation":[{"name":"Faculty of Transport and Traffic Engineering, University of East Sarajevo, 71123 Doboj, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9001-0290","authenticated-orcid":false,"given":"Nenad","family":"Ru\u0161ki\u0107","sequence":"additional","affiliation":[{"name":"Department of Traffic Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Branko","family":"Aleksi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Transport and Traffic Engineering, University of East Sarajevo, 71123 Doboj, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tihomir","family":"\u0110uri\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Transport and Traffic Engineering, University of East Sarajevo, 71123 Doboj, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0704-3084","authenticated-orcid":false,"given":"Lato","family":"Pezo","sequence":"additional","affiliation":[{"name":"Institute of General and Physical Chemistry, University of Belgrade, Studentski Trg 12-16, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2994-6871","authenticated-orcid":false,"given":"Biljana","family":"Lon\u010dar","sequence":"additional","affiliation":[{"name":"Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3285-0520","authenticated-orcid":false,"given":"Milada","family":"Pezo","sequence":"additional","affiliation":[{"name":"Department of Thermal Engineering and Energy, \u201cVIN\u010cA\u201d Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovi\u0107a Alasa 12-14, 11351 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"key":"ref_1","unstructured":"WHO (2023, February 11). 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