Taxi destination prediction based on LSTM with tree memory module

Abstract

Taxi destination prediction can grasp the flow direction of the taxi, facilitate the taxi dispatches. There has always been a long-term dependency problem in taxi trajectory prediction. Although LSTM can solve the long-term dependency problem to a certain extent, it does not have a good ability to deal with the deep correlation between long trajectory sequences. To address the above problem, we propose a taxi destination prediction method based on LSTM with Tree Memory Module (TMM-LSTM). TMM-LSTM stores the state of the input trajectory through an external memory structure. It uses a tree structure to process more historical information and better deal with the long-term relationship between trajectory points. TMM-LSTM can better solve the long-term dependency problem in the taxi trajectory sequence. Experiments demonstrate that the average error distance is 6% lower than traditional LSTM model.