Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays
Accurate traffic flow prediction is essential for highway operations, especially during holidays when surging traffic poses significant challenges. This study focuses on holiday traffic and introduces a spatiotemporal cross-attention network (ST-Cross-Attn) that combines a bidirectional convolutiona...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/7/287 |
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| _version_ | 1839615137646903296 |
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| author | Xiaowei Liu Yunfan Zhang Zhongyi Han Hao Qiu Shuxin Zhang Jinlei Zhang |
| author_facet | Xiaowei Liu Yunfan Zhang Zhongyi Han Hao Qiu Shuxin Zhang Jinlei Zhang |
| author_sort | Xiaowei Liu |
| collection | DOAJ |
| description | Accurate traffic flow prediction is essential for highway operations, especially during holidays when surging traffic poses significant challenges. This study focuses on holiday traffic and introduces a spatiotemporal cross-attention network (ST-Cross-Attn) that combines a bidirectional convolutional LSTM (Bi-ConvLSTM) with a cross-attention module to jointly predict toll station inbound flow and outbound flow. Under the multi-task learning framework, the model shares spatial–temporal features between inbound flow and outbound flow, enhancing their representations and improving multi-step prediction accuracy. Using three years of highway traffic flow data during Labor Day from Shandong, China, ST-Cross-Attn outperformed eight state-of-the-art benchmarks, achieving an average improvement of 4.34% in inbound flow prediction and 2.3% in outbound flow prediction. Extensive ablation studies further confirmed the effectiveness of the model’s components and multi-task learning framework, demonstrating its potential for reliable holiday traffic forecasting. |
| format | Article |
| id | doaj-art-78873133081d4ac29b30df1cdd69de0c |
| institution | Matheson Library |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-78873133081d4ac29b30df1cdd69de0c2025-07-25T13:37:19ZengMDPI AGTechnologies2227-70802025-07-0113728710.3390/technologies13070287Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During HolidaysXiaowei Liu0Yunfan Zhang1Zhongyi Han2Hao Qiu3Shuxin Zhang4Jinlei Zhang5Shandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, ChinaShandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, ChinaShandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, ChinaSchool of Systems Science, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Systems Science, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Systems Science, Beijing Jiaotong University, Beijing 100044, ChinaAccurate traffic flow prediction is essential for highway operations, especially during holidays when surging traffic poses significant challenges. This study focuses on holiday traffic and introduces a spatiotemporal cross-attention network (ST-Cross-Attn) that combines a bidirectional convolutional LSTM (Bi-ConvLSTM) with a cross-attention module to jointly predict toll station inbound flow and outbound flow. Under the multi-task learning framework, the model shares spatial–temporal features between inbound flow and outbound flow, enhancing their representations and improving multi-step prediction accuracy. Using three years of highway traffic flow data during Labor Day from Shandong, China, ST-Cross-Attn outperformed eight state-of-the-art benchmarks, achieving an average improvement of 4.34% in inbound flow prediction and 2.3% in outbound flow prediction. Extensive ablation studies further confirmed the effectiveness of the model’s components and multi-task learning framework, demonstrating its potential for reliable holiday traffic forecasting.https://www.mdpi.com/2227-7080/13/7/287highway traffic flow predictionmulti-task learningspatial–temporal features modeling |
| spellingShingle | Xiaowei Liu Yunfan Zhang Zhongyi Han Hao Qiu Shuxin Zhang Jinlei Zhang Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays Technologies highway traffic flow prediction multi-task learning spatial–temporal features modeling |
| title | Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays |
| title_full | Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays |
| title_fullStr | Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays |
| title_full_unstemmed | Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays |
| title_short | Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays |
| title_sort | multi task learning based traffic flow prediction through highway toll stations during holidays |
| topic | highway traffic flow prediction multi-task learning spatial–temporal features modeling |
| url | https://www.mdpi.com/2227-7080/13/7/287 |
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