mlsanityData to 7 Oct 2026

Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

arXiv 2101.06954 ↗First posted 18 Jan 2021cs.LGcited by 7

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Abstract (arXiv)

Potential crowd flow prediction for new planned transportation sites is a fundamental task for urban planners and administrators. Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites. However, the transportation modes of nearby sites (e.g. bus stations, bicycle stations) might be different from the target site (e.g. subway station), which results in severe data scarcity issues. To this end, we propose a data driven approach, named MOHER, to predict the potential crowd flow in a certain mode for a new planned site. Specifically, we first identify the neighbor regions of the target site by examining the geographical proximity as well as the urban function similarity. Then, to aggregate these heterogeneous relations, we devise a cross-mode relational GCN, a novel relation-specific transformation model, which can learn not only the correlations but also the differences between different transportation modes. Afterward, we design an aggregator for inductive potential flow representation. Finally, an LTSM module is used for sequential flow prediction. Extensive experiments on real-world data sets demonstrate the superiority of the MOHER framework compared with the state-of-the-art algorithms.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 13 of its 33 references that are in the map. First posted on arXiv 18 Jan 2021.

Builds on

13 of 33 references.

References · 13

  1. 2017Inductive Representation Learning on Large Graphscited by 6,284
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  3. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  4. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 119
  5. 2020Inductive Graph Neural Networks for Spatiotemporal Krigingcited by 90
  6. 2019Inductive Matrix Completion Based on Graph Neural Networkscited by 85
  7. 2019Label Efficient Semi-Supervised Learning via Graph Filteringcited by 46
  8. 2018Bike Flow Prediction with Multi-Graph Convolutional Networkscited by 42
  9. 2019Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networkscited by 40
  10. 2019Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Predictioncited by 30
  11. 2020A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputationcited by 27
  12. 2016Latent Space Model for Road Networks to Predict Time-Varying Trafficcited by 22
1 more
  1. 2019Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Developmentcited by 7

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Cited by year

Citing papers per year: 2022: 2; 2023: 5.

7 papers.

  1. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  2. 2023INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Krigingcited by 27
  3. 2023Causal conditional hidden Markov model for multimodal traffic predictioncited by 14
  4. 2023Deep trip generation with graph neural networks for bike sharing system expansioncited by 8
  5. 2022Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networkscited by 7
  6. 2022Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approachcited by 3
  7. 2023A Simple Framework for Multi-mode Spatial-Temporal Data Modelingcited by 0