A traffic flow forecasting method based on the GA-SVR
Aiyun Zhan, Fei Du, Zhaozheng Chen, Guanxiang Yin, Meng Wang, Yuejin Zhang
Abstract
This paper uses support vector regression (SVR) to predict short-term traffic flow, and studies the feasibility of SVR in short-term traffic flow prediction. The short-time traffic flow has many influencing factors, which are characterized by nonlinearity, randomness and periodicity. Therefore, SVR algorithm has advantages in dealing with such problems. In order to improve the prediction accuracy of the SVR, this paper uses genetic algorithm (GA) to optimize the SVR and other parameters to obtain the global optimal solution. The optimal parameters are used to construct the SVR prediction model. This paper selects the traffic flow data of the Jiangxi Provincial Transportation Department database to verify the feasibility and effectiveness of the proposed model.