An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix
Effective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-06-01
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| Series: | Journal of Highway and Transportation Research and Development |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/HTRD.2024.9480015 |
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| _version_ | 1850069203495682048 |
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| author | Wenhao Li Yanyan Chen Yuyan Pan Yunchao Zhang |
| author_facet | Wenhao Li Yanyan Chen Yuyan Pan Yunchao Zhang |
| author_sort | Wenhao Li |
| collection | DOAJ |
| description | Effective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction accuracy. To address these shortcomings, this study proposes the Impedance-Spatio-Temporal Topological Network (Impedance-STTN) prediction model. The Impedance-STTN model integrates K-medoids clustering for data analysis, generating a real-time impedance matrix from impedance functions, traffic big data, and real-time flow data. This approach captures dynamic node relationships within the spatio-temporal network, enhancing prediction accuracy. Experimental results demonstrate the superior predictive performance of the Impedance-STTN model, achieving accuracies of 94.79%, 93.78%, and 93.11% in 5 min, 15 min, and 30 min predictions, respectively. These results outperform existing models, especially in long-term predictions. The findings underscore the model's high accuracy and effectiveness across varying prediction durations, marking a significant advancement in traffic flow prediction. This suggests promising avenues for future research and practical applications. |
| format | Article |
| id | doaj-art-63da166b30bf4f7583a33d7d8b0b08c0 |
| institution | DOAJ |
| issn | 2095-6215 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Highway and Transportation Research and Development |
| spelling | doaj-art-63da166b30bf4f7583a33d7d8b0b08c02025-08-20T02:47:50ZengTsinghua University PressJournal of Highway and Transportation Research and Development2095-62152024-06-01182677510.26599/HTRD.2024.9480015An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance MatrixWenhao Li0Yanyan Chen1Yuyan Pan2Yunchao Zhang3Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaEffective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction accuracy. To address these shortcomings, this study proposes the Impedance-Spatio-Temporal Topological Network (Impedance-STTN) prediction model. The Impedance-STTN model integrates K-medoids clustering for data analysis, generating a real-time impedance matrix from impedance functions, traffic big data, and real-time flow data. This approach captures dynamic node relationships within the spatio-temporal network, enhancing prediction accuracy. Experimental results demonstrate the superior predictive performance of the Impedance-STTN model, achieving accuracies of 94.79%, 93.78%, and 93.11% in 5 min, 15 min, and 30 min predictions, respectively. These results outperform existing models, especially in long-term predictions. The findings underscore the model's high accuracy and effectiveness across varying prediction durations, marking a significant advancement in traffic flow prediction. This suggests promising avenues for future research and practical applications.https://www.sciopen.com/article/10.26599/HTRD.2024.9480015traffic engineeringtraffic predictionspatio-temporal correlationreal time impedance matrixsttn network |
| spellingShingle | Wenhao Li Yanyan Chen Yuyan Pan Yunchao Zhang An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix Journal of Highway and Transportation Research and Development traffic engineering traffic prediction spatio-temporal correlation real time impedance matrix sttn network |
| title | An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix |
| title_full | An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix |
| title_fullStr | An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix |
| title_full_unstemmed | An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix |
| title_short | An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix |
| title_sort | improved spatio temporal network traffic flow prediction method based on impedance matrix |
| topic | traffic engineering traffic prediction spatio-temporal correlation real time impedance matrix sttn network |
| url | https://www.sciopen.com/article/10.26599/HTRD.2024.9480015 |
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