A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
The rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traff...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/779 |
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| author | Yaofang Zhang Jian Chen Jianying Rao |
| author_facet | Yaofang Zhang Jian Chen Jianying Rao |
| author_sort | Yaofang Zhang |
| collection | DOAJ |
| description | The rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traffic distribution, as well as the sparsity and incompleteness of data caused by uneven traffic distribution, make OD traffic prediction complex and challenging. This paper proposes a multi-task prediction model for OD traffic between highway stations. The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning inflow trend modules, sub-task learning outflow trend modules, and main task learning modules for OD traffic. At the same time, the attraction intensity matrix between stations is constructed using the population density data as the external feature of the sub-task module for outlet outflow flow, and stronger constraints between tasks are introduced to achieve better fitting results. Finally, an OD flow prediction case experiment was conducted between stations on highways in Sichuan Province. The experimental results showed that the proposed model not only had higher accuracy in predicting results than other baseline models, but also had better effectiveness and robustness. |
| format | Article |
| id | doaj-art-5f896a17bb8b404c950f41614457724b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5f896a17bb8b404c950f41614457724b2025-01-24T13:20:47ZengMDPI AGApplied Sciences2076-34172025-01-0115277910.3390/app15020779A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway StationsYaofang Zhang0Jian Chen1Jianying Rao2School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaIndustry University Research Cooperation Department, Chongqing Jiaotong University, Chongqing 400074, ChinaThe rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traffic distribution, as well as the sparsity and incompleteness of data caused by uneven traffic distribution, make OD traffic prediction complex and challenging. This paper proposes a multi-task prediction model for OD traffic between highway stations. The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning inflow trend modules, sub-task learning outflow trend modules, and main task learning modules for OD traffic. At the same time, the attraction intensity matrix between stations is constructed using the population density data as the external feature of the sub-task module for outlet outflow flow, and stronger constraints between tasks are introduced to achieve better fitting results. Finally, an OD flow prediction case experiment was conducted between stations on highways in Sichuan Province. The experimental results showed that the proposed model not only had higher accuracy in predicting results than other baseline models, but also had better effectiveness and robustness.https://www.mdpi.com/2076-3417/15/2/779intelligent transportationhighwayOD traffic predictionspatiotemporal characteristicshybrid deep learning model |
| spellingShingle | Yaofang Zhang Jian Chen Jianying Rao A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations Applied Sciences intelligent transportation highway OD traffic prediction spatiotemporal characteristics hybrid deep learning model |
| title | A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations |
| title_full | A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations |
| title_fullStr | A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations |
| title_full_unstemmed | A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations |
| title_short | A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations |
| title_sort | deep multi task learning model for od traffic flow prediction between highway stations |
| topic | intelligent transportation highway OD traffic prediction spatiotemporal characteristics hybrid deep learning model |
| url | https://www.mdpi.com/2076-3417/15/2/779 |
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