Cloud Model-Based Fuzzy Inference System for Short-Term Traffic Flow Prediction

He-Wei Liu, Yi-Ting Wang, Xiao-Kang Wang, Ye Liu, Yan Liu, Xue-Yang Zhang () and Fei Xiao ()
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He-Wei Liu: School of Business, Guilin University of Technology, Guilin 541004, China
Yi-Ting Wang: School of Finance, Hunan University of Finance and Economics, Changsha 410205, China
Xiao-Kang Wang: College of Management, Shenzhen University, Shenzhen 518060, China
Ye Liu: School of Business, Central South University, Changsha 410083, China
Yan Liu: School of Business, Central South University, Changsha 410083, China
Xue-Yang Zhang: Institute of Big Data Intelligent Management and Decision-Making, College of Management, Shenzhen University, Shenzhen 518060, China
Fei Xiao: School of Business, Central South University, Changsha 410083, China

Mathematics, 2023, vol. 11, issue 11, 1-17

Abstract: Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting, which will affect the accuracy and robustness of traffic flow forecasting models. Therefore, this paper proposes a short-term traffic flow forecasting algorithm combining the cloud model and the fuzzy inference system in an uncertain environment, which uses the idea of the cloud model to process the traffic flow data and describe its randomness and fuzziness at the same time. First, the fuzzy c-means algorithm is selected to carry out cluster analysis on the original traffic flow data, and the number and parameter values of the initial membership function of the system are obtained. Based on the cloud reasoning algorithm and the cloud rule generator, an improved fuzzy reasoning system is proposed for short-term traffic flow predictions. The reasoning system cannot only capture the uncertainty of traffic flow data, but it also can describe temporal dependencies well. Finally, experimental results indicate that the proposed model has a better prediction accuracy and better stability, which reduces 0.6106 in RMSE, reduces 0.281 in MAE, and reduces 0.0022 in MRE compared with the suboptimal comparative methods.

Keywords: traffic flow prediction; cloud model; fuzzy inference system; fuzzy C-means (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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