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

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.