Short-term traffic flow prediction based on echo state networks

作者：Yang Fei^*; Wang Chunlu; Zuo Xingquan; Zhong Ruiming; Xiang Fei

来源：Advances in Information Sciences and Service Sciences, 2012, 4(9): 269-277.

DOI：10.4156/AISS.vol4.issue9.33

• Accurate prediction
• Back-propagation neural networks
• Beijing city
• Chaotic properties
• Echo state networks
• Elman neural network
• Highly nonlinear
• Input and outputs
• Key parameters
• Phase space reconstruction
• Prediction accuracy
• Short-term traffic flow
• Traffic flow
• Traffic flow prediction
• Training speed
• Wavelet neural networks

The traffic flow is critical for the traffic control and traffic guidance. As the short-term traffic flow has the characteristics of highly nonlinear, complexity and chaos, the traditional neural networks can not totally satisfy the performance in the aspects of prediction accuracy, training speed and stability. In order to solve those problems, a novel traffic flow prediction model based on echo state networks (ESN) is proposed. The model is established based on the chaotic property of traffic flow. Firstly, through phase space reconstruction of chaotic traffic flow data, the suitable input and output of ESN can be obtained. Then, by optimizing its key parameters of ESN, the ESN can make full use of the chaotic property of the input and output data to get more accurate prediction results. The propose model is applied to a real-world traffic flow prediction problem in the area of Xizhimen bridge of Beijing city, and compare with back-propagation neural networks (BPNN), Elman neural networks (Elman) and wavelet neural networks (WNN). The prediction results show that the model outperforms comparative other models in accuracy, training speed and stability.

• 出版日期2012
• 单位北京大学