Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

Metro origin-destination prediction is a crucial yet challenging time-series analysis task in intelligent transportation systems, which aims to accurately forecast two specific types of cross-station ridership, i.e., Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete OD m...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2023-03, Vol.45 (3), p.3574-3589
Main Authors: Liu, Lingbo, Zhu, Yuying, Li, Guanbin, Wu, Ziyi, Bai, Lei, Lin, Liang
Format: Article
Language:English
Subjects:
Agglomeration
Causality
Causality and correlation
Forecasting
heterogeneous information
Intelligent transportation systems
Mathematical analysis
Mathematical models
Modelling
Neural networks
online metro system
origin-destination ridership
Predictive models
Public transportation
Ridership
Sparse matrices
Task analysis
Temporal distribution
Time series analysis
Transformers
Transportation networks
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Summary:Metro origin-destination prediction is a crucial yet challenging time-series analysis task in intelligent transportation systems, which aims to accurately forecast two specific types of cross-station ridership, i.e., Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete OD matrices of previous time intervals can not be obtained immediately in online metro systems, and conventional methods only used limited information to forecast the future OD and DO ridership separately. In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership. Specifically, an OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices, while a DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Moreover, a Dual Information Transformer is introduced to propagate the mutual information among OD features and DO features for modeling the OD-DO causality and correlation. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously. Extensive experiments conducted on two large-scale benchmarks demonstrate the effectiveness of our method for online metro origin-destination prediction. Our code is resealed at https://github.com/HCPLab-SYSU/HIAM .
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2022.3178184