针对出租车空载率高、司机寻客难的问题,提出泊松-卡尔曼组合预测模型(PKCPM).首先,采用加权非齐次泊松模型,针对出租车历史数据进行建模,得到目标时刻的估计值;其次,基于当天的实时数据,将临近时刻乘客需求的平均值作为目标时刻预测值;最后,将预测值和估计值作为卡尔曼滤波模型的输入参数,实现对目标时刻出租车乘客需求的预测,同时引入误差反向传播机制,减小下一次预测误差.基于郑州市出租车轨迹数据集,对组合模型与非齐次泊松模型(NHPM)、加权非齐次泊松模型(WNHPM)、支持向量机(SVM)等三种模型进行对比,实验结果显示PKCPM的误差比WNHPM、SVM分别降低了8.85个百分点、14.9个百分点.该模型能对不同时段内、不同空间网格的乘客需求进行预测,为出租车寻找乘客提供可靠的依据.%To slove the problem of high taxi empty-loading ratio of taxi and difficulty in finding passengers,a new model called Possion-Kalman combined prediction Model (PKCPM) was proposed.Firstly,weighted Non-Homogeneous Poisson Model (NHPM) was used to get the estimated value of the target time based on taxi historical data.Secondly,the mean value of the passenger demand in the near time,was taken as the predicted value,based on the real-time data.Finally,the predicted value and the estimated value were used as the inputs of Kalman filtering model to predict the target variance,meanwhile,the error backpropagation mechanism was introduced to reduce the next prediction error.The experimental results on the taxi trajectory dataset in Zhengzhou show that compared with NHPM,Weighted NHPM (WNHPM) and Support Vector Machine (SVM),PKCPM achieves a better optimization effect,and the error of PKCPM is reduced by about 8.85 percentage points and 14.9 percentage points respectively compared with WNIIPM and SVM.PKCPM can predict passenger demand within different time and spacial grid,and provides a reliable solution to taxi driver for finding passengers.
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