基于支持向量回归机的交通状态短时预测和北京某区域实例分析

根据基于支持向量回归机的交通状态短时预测方法建立了数学模型,考虑以交通检测器收集到所要预测时刻前几个时段及被测路段上下游前几时段的交通流量、车道占有率、平均线速度等交通参数为输入,以对应时段的平均线速度为输出.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,利用输入参数预测下时段的交通线速度.最后,以北京市北四环某路段的实时监测数据来对模型进行检测,预测结果表明了模型的有效性.

Short-term Traffic Forecasting Based on Support Vector Regression and an Analysis on a Real Regional Experiment in Beijing

The paper proposes a short-term traffic forecasting model based on support vector regression.First,themodel put the traffic volumes,occupancy-rate,average velocity at several preceding periods of time and upstream and downstream collected by RTMS are considered sa input,average velocity at current period of time are considered as output .Second,the support vector regression is trained after selecting a kernel function.Finally, the average velocity 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 use the real time date of some road section of North Beingjing links to test the efficiency of the proposed model and the result is satisfied.