关键词: 出租车目的地预测, 循环神经网络, SRTDP方法, 预测准确率

Abstract: In the prediction of the taxi destination, the traditional Markov prediction method relies only on the first 2 to 3 GPS points, and does not apply to trajectories that have very long dependencies. In order to solve the long-term dependencies, this paper uses Recurrent Neural Network（RNN） to predict the taxi destination, this is because the multiple hidden layers of RNN can store this dependencies. However, with the increasing amount of data, the hidden layers of RNN is very sensitive to small perturbations and the perturbations will be exponentially enlarge in the latter part of prediction, reducing the prediction accuracy. In order to improve the prediction accuracy of taxi destination and reduce the training time, this paper applies SDZ to RNN, and proposes a new taxi destination prediction method based on SDZ-RNN（SRTDP）. SDZ can not only improve the robustness of SRTDP, but also reduce the training time by adopting partial update instead of full update. Experiments show that SRTDP is superior to RNN prediction method in accuracy and speed, the prediction accuracy is improved by 12%, and the training completion time is reduced by 7%.

Key words: taxi destination prediction, recurrent neural networks, SRTDP method, prediction accuracy