基于支持向量回归机的交通状态短时预测方法研究

提出基于支持向量回归机的交通状态短时预测方法.具体的做法是,以交通检测器收集到某时刻前几时段及上下游前几时段的交通流量、占有率、平均速度等交通参数为输入,以对应时段交通流量为输出,选取核函数,对支持向量回归机进行训练.应用训练完成的支持向量回归机,输入交通流量、占有率、平均速度,来预测下时段的交通流量.最后,以某城市道路的实时数据来对模型进行验证,预测结果表明了模型的有效性.

Research on Methods of Short-Term Traffic Forecasting Based on Support Vector Regression

The paper proposes-short-term traffic forecasting model based on support vector regression. First, the traffic volumes, occupancy-rate, average velocity at several preceding periods of time and upstream and downstream collected by RTMS are considered as input, traffic volumes at current period of time are considered as output. Second, the support vector regression is trained after selecting a kernel function. Finally, the traffic volumes being forecasted at several periods of time in the future are available by inputting the traffic volumes, occupancy-rate and average velocity necessary to the trained support vector regression. The paper also uses the real time data of certain urban road to test the efficiency of the proposed model and the result is satisfied.