基于加权时变泊松模型的出租车载客点推荐模型_商建东1

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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 WNHPM and SVM． PKCPM can predict passenger demand

30 min