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A Light Weight Traffic Volume Prediction Approach Based on Finite Traffic Volume Data

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Abstract

As one of the key technologies of intelligent transportation systems, short-term traffic volume prediction plays an increasingly important role in solving urban traffic problems. In the last decade, many approaches were proposed for the traffic volume prediction from different perspectives. However, most of these approaches are based on a large amount of historical data. When there are only finite collected traffic data, they cannot be well trained, so the prediction accuracy of these approaches will be poor. In this paper, a tensor model is proposed to capture the change patterns of continuous traffic volumes. From collected traffic volume data, the element data are extracted to update the corresponding elements of the tensor model. Then, a tucker decomposition and gradient descent based algorithm is employed to impute the missing elements of the tensor model. After missing element imputation, the tensor model can be directly applied to the short-term traffic volume prediction through searching the corresponding elements of the model and the storage cost of the model is low. Our model is evaluated on real traffic volume data from PeMS dataset, which indicates that our model has higher traffic volume prediction accuracy than other approaches in the situation of finite traffic volume data.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the PeMS repository, pems.dot.ca.gov.

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Acknowledgments

We gratefully thank all reviewers for their time spend making their constructive remark and useful suggestions, which has significantly raised the quality of the manuscript and has enable us to improve the manuscript.

This work is supported by the National Natural Science Foundation of China (No. 62276011, 62072016), the Natural Science Foundation of Beijing Municipality (No. 4212016), Urban Carbon Neutral Science and Technology Innovation Fund Project of Beijing University of Technology (No. 040000514122608).

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Bering University of Technology, Beijing, 100124, China

    Xing Su, Minghui Fan & Zhi Cai

  2. Shanghai Ocean University, Shanghai, 201306, China

    Qing Liu

  3. Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, 100094, China

    Xiaojun Zhang

Authors
  1. Xing Su
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  2. Minghui Fan
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  3. Zhi Cai
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  4. Qing Liu
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  5. Xiaojun Zhang
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Corresponding author

Correspondence to Qing Liu.

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The authors declare no competing interests.

Additional information

Xing Su is an associate professor in the Faculty of Information Technology, Beijing University of Technology, China. He received his B.Sc in the School of Software Engineering from Beijing University of Technology in 2007. He received his M.Sc. and Ph.D. in computer science from University of Wollongong, Australia in 2012 and 2015, respectively. His research interests include multi-agent systems, wireless sensor network and service-oriented computing.

Minghui Fan is a M.Sc. candidate of College of Computer Science, Beijing University of Technology. She obtained her bachelor degree in 2019 from the School of Computer Science and Technology in Shandong University of Technology. Her main research interests include multi-agent system and machine learning.

Zhi Cai is an associate professor in the College of Computer Science, Bejjing University of Technology, China. He obtained his M.Sc. in 2007 from the School of Computer Science in the University of Manchester and his Ph.D. in 2011 from the Department of Computing and Mathematics of the Manchester Metropolitan University, U.K. His research interests include information retrieval, ranking in relational databases, keyword search, intelligent transportation systems.

Qing Liu is a lecturer in College of Marine Culture and Law, Shanghai Ocean University, China. She received B.Sc. in sociology from Shaanxi Normal University in 2006. She received M.Sc in sociology from Xi’an Jiaotong University in 2009. She received Ph.D. in Renmin University of China in 2017. Her research interests include the statistics, network sociology, economic sociology and sociology of law.

Xiaojun Zhang is currently a research associate at Academy of Opto-Electronics, Chinese Academy of Sciences. He received Ph.D. degree from University of Wollongong, Australia in 2015. His research interests largely lie in the areas of machine learning, embedded systems, and big data. He has published more than 10 research papers on the well-known journals and conference proceedings. He has been responsible for 1 NSFC project and several ministerial projects.

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Su, X., Fan, M., Cai, Z. et al. A Light Weight Traffic Volume Prediction Approach Based on Finite Traffic Volume Data. J. Syst. Sci. Syst. Eng. 32, 603–622 (2023). https://doi.org/10.1007/s11518-023-5572-x

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