NDLI: Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features

Content Provider	IET Digital Library
Author	Yang, Dan Chen, Kairun Yang, Mengning Zhao, Xiaochao
Abstract	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.
Starting Page	1475
Ending Page	1482
Page Count	8
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 10, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5511
Journal	IET Intelligent Transport Systems
Publisher Date	2019-06-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate Forecast ELF-LSTM Forecasting Theory Improved Model Enhanced Long-term Feature Knowledge Engineering Technique Learning in AI Long Temporal Dependence Long Temporal Dependency Feature Long-short-term Memory LSTM Neural Network Model LSTM NN Neural Computing Technique Neural Nets Origin Destination Flow Outbreak Passenger Flow Passenger Flow Data Rail Traffic Rail Transit Congestion Rail Transit System Recurrent Neural Nets Road Traffic Short-term Feature Statistics System Theory Application in Transportation Time Series Traffic Congestion Traffic Control Traffic Control Department Traffic Engineering Computing Traffic Sequence Transportation Typical Time Series Urban Rail Transit Passenger Flow
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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