Models for forecasting the traffic flow within the city of Ljubljana
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, e...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-09-01
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| Series: | European Transport Research Review |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12544-023-00600-6 |
| _version_ | 1797578459065614336 |
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| author | Gašper Petelin Rok Hribar Gregor Papa |
| author_facet | Gašper Petelin Rok Hribar Gregor Papa |
| author_sort | Gašper Petelin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:22:12Z |
| format | Article |
| id | doaj.art-5b2bf01fa91c411c9a0aa3e2e8563401 |
| institution | Directory Open Access Journal |
| issn | 1866-8887 |
| language | English |
| last_indexed | 2024-03-10T22:22:12Z |
| publishDate | 2023-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Transport Research Review |
| spelling | doaj.art-5b2bf01fa91c411c9a0aa3e2e85634012023-11-19T12:13:56ZengSpringerOpenEuropean Transport Research Review1866-88872023-09-0115112010.1186/s12544-023-00600-6Models for forecasting the traffic flow within the city of LjubljanaGašper Petelin0Rok Hribar1Gregor Papa2Computer Systems Department, Jožef Stefan InstituteComputer Systems Department, Jožef Stefan InstituteComputer Systems Department, Jožef Stefan InstituteAbstract 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.https://doi.org/10.1186/s12544-023-00600-6Traffic modelingTime-series forecastingTraffic-count data setMachine learningModel comparison |
| spellingShingle | Gašper Petelin Rok Hribar Gregor Papa Models for forecasting the traffic flow within the city of Ljubljana European Transport Research Review Traffic modeling Time-series forecasting Traffic-count data set Machine learning Model comparison |
| title | Models for forecasting the traffic flow within the city of Ljubljana |
| title_full | Models for forecasting the traffic flow within the city of Ljubljana |
| title_fullStr | Models for forecasting the traffic flow within the city of Ljubljana |
| title_full_unstemmed | Models for forecasting the traffic flow within the city of Ljubljana |
| title_short | Models for forecasting the traffic flow within the city of Ljubljana |
| title_sort | models for forecasting the traffic flow within the city of ljubljana |
| topic | Traffic modeling Time-series forecasting Traffic-count data set Machine learning Model comparison |
| url | https://doi.org/10.1186/s12544-023-00600-6 |
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