Open AccessUrban rail transit passenger flow forecast based on LSTM with enhanced long‐term features
Author(s) -
Yang Dan,
Chen Kairun,
Yang Mengning,
Zhao Xiaochao
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5511
Subject(s) - computer science , dependency (uml) , urban rail transit , term (time) , recurrent neural network , lag , artificial neural network , time series , real time computing , traffic flow (computer networking) , flow (mathematics) , traffic congestion , long short term memory , deep learning , artificial intelligence , machine learning , transport engineering , engineering , computer network , physics , geometry , mathematics , quantum mechanics
Outbreak passenger flow is the main cause of rail transit congestion. In this regard, the accurate forecast of passenger flow in advance will facilitate the traffic control department to redeploy infrastructures. Traffic sequence is a typical time series with long temporal dependence. For instance, an emergency may cause traffic congestion for the next several hours. Only a few studies focused on the way to capture long temporal dependence of passenger flow in the rail transit system. Here, an improved model enhanced long‐term features based on long‐short‐term memory (ELF‐LSTM) neural network is proposed. It takes full advantages of LSTM Neural Network (LSTM NN) models in processing time series and overcomes its limitations in insufficient learning of long temporal dependency due to time lag. The proposed network strengthens the long temporal dependency features embedded in passenger flow data and incorporates the short‐term features to predict the origin destination (OD) flow in the next hour. The experiment results show that ELF‐LSTM outperforms other state‐of‐the‐art methods in terms of forecasting.