NDLI: Deep learning-based hybrid model for short-term subway passenger flow prediction using automatic fare collection data

Content Provider	IET Digital Library
Author	Jia, Feifan Li, Haiying Jiang, Xi Xu, Xinyue
Abstract	Short-term passenger flow prediction is one key prerequisite of decision making in daily operations. Subway operators concentrate on both the efficiency of the prediction model and the accuracy of the prediction results. In this study, the authors explore a deep learning-based hybrid model, which integrates a long–short-term memory neural network (LSTM NN) and stacked auto-encoders (SAEs), for predicting short-term passenger flows of each station in a subway network simultaneously. SAEs are employed to extract network passenger flow data features, which involves mapping high-dimensionality data to low-dimensionality data at the first stage. Then, LSTM NN is trained using low-dimensionality data. Finally, SAEs restore the predicted data output by LSTM NN to predict the network passenger flow data. By employing automatic fare collection data of the Guangzhou subway, the proposed hybrid model was evaluated. The experimental results show that the average mean relative error of the proposed hybrid model is 4.6%, which is much better than other current prediction models.
Starting Page	1708
Ending Page	1716
Page Count	9
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 11, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0112
Journal	IET Intelligent Transport Systems
Publisher Date	2019-07-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Fare Collection Data Backpropagation Current Prediction Model Daily Operations Deep Learning-based Hybrid Model Guangzhou Subway Knowledge Engineering Technique Learning in AI Low-dimensionality Data LSTM NN Mapping High-dimensionality Data Network Passenger Flow Data Feature Neural Computing Technique Neural Nets Predicted Data Output Prediction Model Prediction Result SAEs Short-term Passenger Flow Prediction Short-term Passenger Flows Short-term Subway Passenger Flow Prediction Stacked Auto-encoders Statistics Subway Network Subway Operators Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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