scholarly journals Short-Term Traffic Flow Prediction Considering Spatio-Temporal Correlation: A Hybrid Model Combing Type-2 Fuzzy C-Means and Artificial Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 101009-101018 ◽  
Author(s):  
Jinjun Tang ◽  
Lexiao Li ◽  
Zheng Hu ◽  
Fang Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Hybrid Model ◽  
Temporal Correlation ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal

scholarly journals Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

2020 ◽  
Vol 32 (6) ◽  
pp. 747-760
Author(s):  
Changxi Ma ◽  
Limin Tan ◽  
Xuecai Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Prediction Accuracy ◽  
Feedforward Neural Network ◽  
Short Term ◽  
Combined Model ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Artificial Neural

In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model.

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