Taxi destination prediction based on LSTM with tree memory module

Dan Song; Yadong Li; Meng Zhang; Ting Zhang

doi:10.1117/12.2667488

28 March 2023 Taxi destination prediction based on LSTM with tree memory module

Dan Song, Yadong Li, Meng Zhang, Ting Zhang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125660I (2023) https://doi.org/10.1117/12.2667488
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

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.

Citation Download Citation

Dan Song, Yadong Li, Meng Zhang, and Ting Zhang "Taxi destination prediction based on LSTM with tree memory module", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125660I (28 March 2023); https://doi.org/10.1117/12.2667488

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