Volume 25 Issue 1
Feb.  2025
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WANG Yi-bing, HU Ran, YU Hong-xin, LI Jia-heng, ZHANG Yu-jie, XU Zhi-gang, HE Zhao-cheng, LU Qi-rong. Global traffic state prediction method for non-sensing locations on freeways[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 274-294. doi: 10.19818/j.cnki.1671-1637.2025.01.020
Citation: WANG Yi-bing, HU Ran, YU Hong-xin, LI Jia-heng, ZHANG Yu-jie, XU Zhi-gang, HE Zhao-cheng, LU Qi-rong. Global traffic state prediction method for non-sensing locations on freeways[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 274-294. doi: 10.19818/j.cnki.1671-1637.2025.01.020
shu

Global traffic state prediction method for non-sensing locations on freeways

doi: 10.19818/j.cnki.1671-1637.2025.01.020
  • 1.

    College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, Zhejiang, China

  • 2.

    College of Civil Engineering, Tongji University, Shanghai 200092, China

  • 3.

    Zhejiang Communications Investment Group Co., Ltd., Hangzhou 310020, Zhejiang, China

  • 4.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 5.

    School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, Guangdong, China

Funds:

National Natural Science Foundation of China 52272315

Key Research and Development Program of Zhejiang Province 2024C01180

Key Research and Development Program of Zhejiang Province 2022C01129

International Science and Technology Cooperation Project of Ningbo 2023H020

More Information
  • Corresponding author: WANG Yi-bing(1968-), male, professor, PhD, wangyibing@zju.edu.cn
  • Received Date: 2024-05-19
  • Publish Date: 2025-02-25
    Abstract
  • In view of the problem that existing research on traffic state prediction of freeways rarely considers non-sensing locations or road topology changes, the limitations of existing research methods were analyzed. A traffic state prediction method combining macroscopic traffic flow model, extended Kalman filtering, and data-driven long short-term memory (LSTM) was proposed, aiming to fully leverage the advantages of machine learning in temporal feature expression and trustworthy traffic flow models in spatial dynamic tracking. Based on the flow and speed data of limited sensing locations, a model of ecoulement of traffic autoroute for networks (METANET) was constructed, and the global model parameters and fundamental diagram parameters were calibrated. A traffic state estimator based on METANET and extended Kalman filtering was designed. The machine learning model was trained to predict the traffic state of all sensing points, and the traffic state estimator was driven to predict the global traffic state. Research results show that the proposed traffic state prediction method can significantly improve the prediction accuracy of flow and speed of freeways. The mean absolute percentage errors of 5-minute flow and speed predictions are 6.92% and 5.29%, which perform 29.62% and 24.28% better than baseline method, respectively, and those of 30-minute flow and speed predictions are 10.02% and 8.62%, which perform 24.84% and 15.87% better than baseline method, respectively. In addition, the proposed method fully considers the impact of on/off ramp flow on the mainline traffic state, so the performance of mainline traffic flow prediction is significantly improved.

     

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