Open AccessResearch on GSTAR-SVM Traffic Prediction Model Based on Wavelet Transform
Author(s) -
Jinghao Hu,
Shandong Wang,
Jian Mao
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1345/3/032009
Subject(s) - support vector machine , computer science , traffic flow (computer networking) , data mining , wavelet transform , research object , traffic congestion , wavelet , artificial intelligence , predictive modelling , machine learning , engineering , transport engineering , geography , computer security , regional science
With the continuous advancement of urbanization in China, the number of urban vehicles has increased rapidly, and traffic congestion has become an urgent problem for modern cities. Therefore, accurate real-time traffic flow prediction is of great significance for solving this problem. However, traffic flow, as a special type of prediction object, has the characteristics of complexity, uncertainty and nonlinearity, which bring great difficulties to the prediction, and the external spatial correlation also has a great influence on the prediction results. In this paper, based on the above problems, the GSTAR-SVM traffic prediction model based on wavelet transform is constructed to predict the short-term traffic flow, and the model is verified based on the data. The experimental results show that the proposed model has higher prediction accuracy.