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篇名 |
Short-time Traffic Flow Prediction Based on Wavelet Neural Network
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並列篇名 | Short-time Traffic Flow Prediction Based on Wavelet Neural Network |
作者 | Li Liu、Peipei Wu、Xinzhe Wang |
英文摘要 | With the rapid development of social economy, traffic congestion, frequent traffic accidents, and traffic pollution have become the focus of attention. As an important part of intelligent transportation system, traffic guidance system and traffic control system play a great role in improving road congestion, Therefore, the shorttime traffic flow prediction is of great significance to urban traffic system. Based on the investigation of traffic flow characteristics, we analyze the advantages and disadvantages of available short-time traffic flow forecasting methods. In view of the nonlinear, timevarying and uncertain characteristics of traffic flow, wavelet neural network is selected as the traffic flow forecasting model combining the self-learning and selfadaptive characteristics of neural network. To solve the problem of short-time prediction and accuracy of traffic conditions, wavelet basis is used as the characteristic function of pattern recognition to optimize the parameter and error space of traditional BP neural network, the short-time prediction of traffic state is realized. In this paper, the wavelet neural network model is established, the traffic flow sample set is constructed, the sample data is trained to convergence, and a certain number of sample data are selected for testing. The test results show that the wavelet neural network not only has high prediction accuracy but also has fast convergence speed, good realtime performance, and has certain application value. |
起訖頁 | 1237-1246 |
關鍵詞 | Short-time traffic flow、Wavelet analysis、Neural network |
刊名 | 網際網路技術學刊 |
期數 | 201907 (20:4期) |
出版單位 | 台灣學術網路管理委員會 |
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