Dynamic Relational Graph Convolutional Network for Metro Passenger Flow Forecasting

Bisheng He (), Yongjun Zhu (), Andrea D’Ariano (), Keyu Wen () and Lufeng Chen ()
Additional contact information
Bisheng He: Southwest Jiaotong University
Yongjun Zhu: Southwest Jiaotong University
Andrea D’Ariano: Roma Tre University
Keyu Wen: China Railway Economic and Planning Research Institute
Lufeng Chen: University of Electronic Science and Technology of China

SN Operations Research Forum, 2023, vol. 4, issue 4, 1-27

Abstract: Abstract Due to the complex network structure and time-varying spatial relationships inherent in metro station systems, coupled with the influence of historical time series, station-level passenger flow forecasting tasks are challenging to solve by traditional prediction models. Therefore, we propose a dynamic relational graph convolutional network that includes a spatial-temporal framework to forecast passenger flow. Regarding spatial convolution, apart from physical and similarity graphs, we introduce accessibility graphs, which utilize the origin–destination (OD) matrix and the OD path consumption time, into graph convolutional networks. Then, dynamic graph convolution by the long short-term memory (LSTM) model is implemented with similarity graphs and accessibility graphs. Within the temporal convolution, the temporal convolutional network (TCN) module is implemented to convolve historical passenger flow to acquire passenger flow in the near future. Moreover, an automatic fare collection (AFC) dataset of the Chongqing metro system is adopted to validate the effectiveness of our proposed model with the baselines. The experimental results confirm that our model outperforms the baselines and show the effectiveness of the dynamic multirelationship and the TCN module.

Keywords: Metro system; Passenger flow forecasting; Dynamic graph convolution; Accessibility graphs; Temporal convolutional networks (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s43069-023-00266-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:4:y:2023:i:4:d:10.1007_s43069-023-00266-9

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-023-00266-9

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().