| Title: | Models for forecasting the traffic flow within the city of Ljubljana |
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| Authors: | ID Petelin, Gašper, Institut Jožef Stefan (Author)
ID Hribar, Rok, Institut Jožef Stefan (Author)
ID Papa, Gregor, Institut Jožef Stefan (Author) |
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| Files: |  URL - Source URL, visit https://etrr.springeropen.com/articles/10.1186/s12544-023-00600-6
 PDF - Presentation file, download (5,05 MB)
MD5: A7A63957C451546C7DC2A80B401C5069
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | 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. |
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| Keywords: | traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 03.01.2023 |
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| Article acceptance date: | 28.08.2023 |
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| Publication date: | 07.09.2023 |
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| Publisher: | Springer Nature |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 1-20 |
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| Numbering: | Vol. 15, article no. 30 |
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| Source: | Švica |
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| PID: | 20.500.12556/DiRROS-17091  |
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| UDC: | 656 |
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| ISSN on article: | 1866-8887 |
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| DOI: | 10.1186/s12544-023-00600-6 |
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| COBISS.SI-ID: | 163745027 |
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| Copyright: | © The Author(s) 2023. |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 8. 9. 2023;
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| Publication date in DiRROS: | 28.09.2023 |
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| Views: | 770 |
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| Downloads: | 331 |
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| Metadata: |    |
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