关键词: 交通控制, 短时交通流, 预测模型, 机器学习, 支持向量机回归

Abstract:

As the short- term traffic flow forecasting theories and approaches help to improve the ability of traffic control systems to automatically adapt to traffic flow changes,this paper proposes a short- term traffic flow forecasting model based on the support vector machine regression by using a kernel function to transform the issues into a linearregression problem in Hilbert Space.Then,the corresponding experiments are conducted based on the data from the traffic flow detection systems in Guangzhou.It is found that the forecasted results accord well with the actual data,and that the forecasting error of the proposed model is less than those of the prediction methods based on Kalman filtering.Thus,the feasibility and effectiveness of the proposed model are verified.

Key words: traffic control, short- term traffic flow, forecasting model, machine learning, support vector machine regression