Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM

Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM

Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive...

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Bibliographic Details
Main Authors: Haijun Li, Yongpeng Zhao, Changxi Ma, Ke Wang, Xiaoting Huang, Wentao Zhang
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2022/2589681
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