Abstract
Traffic flow prediction plays an important role in intelligent transportation applications, such as traffic control, navigation, path planning, etc., which are closely related to people’s daily life. In the last twenty years, many traffic flow prediction approaches have been proposed. However, some of these approaches use the regression based mechanisms, which cannot achieve accurate short-term traffic flow predication. While, other approaches use the neural network based mechanisms, which cannot work well with limited amount of training data. To this end, a light weight tensor-based traffic flow prediction approach is proposed, which can achieve efficient and accurate short-term traffic flow prediction with continuous traffic flow data in a limited period of time. In the proposed approach, first, a tensor-based traffic flow model is proposed to establish the multi-dimensional relationships for traffic flow values in continuous time intervals. Then, a CANDECOMP/PARAFAC decomposition based algorithm is employed to complete the missing values in the constructed tensor. Finally, the completed tensor can be directly used to achieve efficient and accurate traffic flow prediction. The experiments on the real dataset indicate that the proposed approach outperforms many current approaches on traffic flow prediction with limited amount of traffic flow data.
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Acknowledgments
This work is supported by the Beijing Natural Science Foundation under Nos. 4192004 and 4212016, the National Natural Science Foundation of China under Grant Nos. 61703013 and 62072016,, the Project of Beijing Municipal Education Commission under Grant Nos. KM201810005024 and KM201810005023, Foundation from School of Computer Science and Technology, Beijing University of Technology under Grants No. 2020JSJKY005 the International Research Cooperation Seed Fund of Beijing University of Technology under Grant No. 2021B29 and National Engineering Laboratory for Industrial Big-data Application Technology.
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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 PhD in computer science from University of Wollongong, Australia in 2012 and 2015. His research interests include distributed artificial intelligence, multiagent systems, disaster management 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.
Minjie Zhang is a full professor in the School of Computing and Information Technology and the Director of Intelligent System Research Centre in the Faculty of Engineering and Information Science, at University of Wollongong, Australia. She received her BSc. degree from Fudan University, China, in 1982 and the PhD degree in computer science from the University of New England, Australia, in 1996. Her research interests include distributed artificial intelligence, multi-agent systems, agent simulation and modeling in complex domains, grid computing, and knowledge discovery and data mining.
Yi Liang is an associate professor in the Faculty of Information Technology, Beijing University of Technology, China. She obtained her M.Sc. in 2000 from the College of Computer Science in Huazhong University of Science and Technology and Ph.D. in 2005 from the Institute of Computing Technology, Chinese Academy of Sciences. Her research interests include big data systerms, high performance computing, service computing.
Limin Guo is a lecturer at the Beijing University of Technology. Her research interests include database research and implementation, spatial-temporal data mining, etc. She received her bachelor’s degree from Huazhong University of Science and Technology in 2005 and PhD degree in the Institute of Software, Chinese Academy of Sciences in 2012.
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Su, X., Fan, M., Zhang, M. et al. An Innovative Approach for the Short-term Traffic Flow Prediction. J. Syst. Sci. Syst. Eng. 30, 519–532 (2021). https://doi.org/10.1007/s11518-021-5492-6
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DOI: https://doi.org/10.1007/s11518-021-5492-6