mlsanityData to 7 Oct 2026

Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

arXiv 2107.00946 ↗First posted 2 Jul 2021cs.LGcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 42 of its 83 references that are in the map. First posted on arXiv 2 Jul 2021.

Builds on

42 of 83 references.

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 4; 2025: 0; 2026: 3.

11 papers.

  1. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  2. 2021Road Network Guided Fine-Grained Urban Traffic Flow Inferencecited by 4
  3. 2021Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Datacited by 2
  4. 2023Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformercited by 2
  5. 2024ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Predictioncited by 2
  6. 2024Multi-Graph Inductive Representation Learning for Large-Scale Urban Rail Demand Prediction under Disruptionscited by 1
  7. 2024UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Methodcited by 0
  8. 2024CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow trackingcited by 0
  9. 2026Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptionscited by 0
  10. 2026Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Predictioncited by 0
  11. 2026Inferring Urban Mobility Interactions from Aggregated Dynamicscited by 0