基于免疫算法优化LSSVM的短时交通流预测

Short-term traffic flow prediction based on LSSVM optimized by immune algorithm

为了智能化解决城市道路交通系统存在的问题,提高短时交通流预测的准确性,采用免疫算法优化的最小二乘支持向量机(LSSVM)建立短时交通流量预测模型。利用免疫算法对LSSVM中的惩罚因子和核函数参数进行优化,得到最优预测模型。以车辆行驶平均速度和占有率作为模型输入,交通流量作为输出进行预测仿真试验。试验结果表明:本文采用的优化LSSVM模型进行仿真试验的预测误差有所减小,输出结果更接近真实值。

In order to intelligently solve the problems existing in urban road networks and improve the accuracy of short-term traffic flow prediction,a short-term traffic prediction model is established by using the least square support vector machine(LSSVM).Specifically,the immune algorithm is adopted to optimize the penalty factor and kernel parameters of the LSSVM,thus obtaining the optimal prediction model.The prediction simulation experiment takes the average speed and occupancy rate of vehicles as the input of the model to predict the traffic flow.The experimental results show that the prediction error of the optimized LSSVM model used in the simulation experiment is reduced,and the output result is closer to the real value.