20.500.12556/DiRROS-17091 Models for forecasting the traffic flow within the city of Ljubljana Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data. traffic modeling time-series forecasting traffic-count data set machine learning model comparison modeliranje prometa napovedovanje časovnih vrst modeliranje količine prometa strojno učenje primerjava modelov true false true Springer Nature Švica Angleški jezik Slovenski jezik © The Author(s) 2023. Neznano 2023-09-28 09:23:24 2023-09-28 09:23:26 2023-09-29 03:35:22 0000-00-00 00:00:00 2023 0 0 Nasl. z nasl. zaslona; Opis vira z dne 8. 9. 2023; str. 1-20 Vol. 15, article no. 30 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-01-03 2023-08-28 2023-09-07 656 1866-8887 10.1186/s12544-023-00600-6 163745027 https://etrr.springeropen.com/articles/10.1186/s12544-023-00600-6 1 25fa6b02-5dbf-11ee-8cc8-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=23204 RAZ_Petelin_Gasper_2023.pdf RAZ_Petelin_Gasper_2023.pdf 1 A7A63957C451546C7DC2A80B401C5069 01b83a622e28abb8ecca309fd36c53c64dd76718f1487a4be16e1a95656c4e5f 5b692e27-5dc0-11ee-8cc8-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=23205 Institut Jožef Stefan 0 0 0