A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction
Joint Authors
Bao, Jie
He, Jie
Hong, Qiong
Shi, Xiaomeng
Zhang, Hao
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-30
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The primary objective of this study is to predict the short-term metro passenger flow using the proposed hybrid spatiotemporal deep learning neural network (HSTDL-net).
The metro passenger flow data is collected from line 2 of Nanjing metro system to illustrate the study procedure.
A hybrid spatiotemporal deep learning model is developed to predict both inbound and outbound passenger flows for every 10 minutes.
The results suggest that the proposed HSTDL-net achieves better prediction performance on suburban stations than on urban stations, as well as generating the best prediction accuracy on transfer stations in terms of the lowest MAPE value.
Moreover, a comparative analysis is conducted to compare the performance of proposed HSTDL-net with other typical methods, such as ARIMA, MLP, CNN, LSTM, and GBRT.
The results indicate that, for both inbound and outbound passenger flow predictions, the HSTDL-net outperforms all the compared models on three types of stations.
The results suggest that the proposed hybrid spatiotemporal deep learning neural network can more effectively and fully discover both spatial and temporal hidden correlations between stations for short-term metro passenger flow prediction.
The results of this study could provide insightful suggestions for metro system authorities to adjust the operation plans and enhance the service quality of the entire metro system.
American Psychological Association (APA)
Zhang, Hao& He, Jie& Bao, Jie& Hong, Qiong& Shi, Xiaomeng. 2020. A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1175647
Modern Language Association (MLA)
Zhang, Hao…[et al.]. A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction. Journal of Advanced Transportation No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1175647
American Medical Association (AMA)
Zhang, Hao& He, Jie& Bao, Jie& Hong, Qiong& Shi, Xiaomeng. A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1175647
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175647