mlsanityData to 7 Oct 2026

Deep trip generation with graph neural networks for bike sharing system expansion

arXiv 2303.11977 ↗First posted 20 Mar 2023cs.LG cs.AIcited by 8

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Abstract (arXiv)

Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-scale pilot and gradually expand the system to cover more areas. For station-based BSSs, this means planning new stations based on existing ones over time, which requires prediction of the number of trips generated by these new stations across the whole system. Previous studies typically rely on relatively simple regression or machine learning models, which are limited in capturing complex spatial relationships. Despite the growing literature in deep learning methods for travel demand prediction, they are mostly developed for short-term prediction based on time series data, assuming no structural changes to the system. In this study, we focus on the trip generation problem for BSS expansion, and propose a graph neural network (GNN) approach to predicting the station-level demand based on multi-source urban built environment data. Specifically, it constructs multiple localized graphs centered on each target station and uses attention mechanisms to learn the correlation weights between stations. We further illustrate that the proposed approach can be regarded as a generalized spatial regression model, indicating the commonalities between spatial regression and GNNs. The model is evaluated based on realistic experiments using multi-year BSS data from New York City, and the results validate the superior performance of our approach compared to existing methods. We also demonstrate the interpretability of the model for uncovering the effects of built environment features and spatial interactions between stations, which can provide strategic guidance for BSS station location selection and capacity planning.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 16 of its 44 references that are in the map. First posted on arXiv 20 Mar 2023.

Builds on

16 of 44 references.

References · 16

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2019Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Predictioncited by 64
  4. 2020Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic informationcited by 58
  5. 2018Bike Flow Prediction with Multi-Graph Convolutional Networkscited by 42
  6. 2020MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendationcited by 38
  7. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  8. 2017Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approachcited by 35
  9. 2022Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networkscited by 29
  10. 2020Theory-based residual neural networks: A synergy of discrete choice models and deep neural networkscited by 15
  11. 2019Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Developmentcited by 7
  12. 2021Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Predictioncited by 7
4 more
  1. 2022Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networkscited by 7
  2. 2018Cross-City Transfer Learning for Deep Spatio-Temporal Predictioncited by 6
  3. 2022Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approachcited by 3
  4. 2019Demand Prediction for Electric Vehicle Sharingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 1.

8 papers.

  1. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  2. 2024Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approachcited by 6
  3. 2023ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobilitycited by 2
  4. 2025Can Moran Eigenvectors Improve Machine Learning of Spatial Data? Insights from Synthetic Data Validationcited by 1
  5. 2025Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learningcited by 0
  6. 2025Generating Individual Travel Diaries Using Large Language Models Informed by Census and Land-Use Datacited by 0
  7. 2025A Ranking-Based Optimization Algorithm for the Vehicle Relocation Problem in Car Sharing Servicescited by 0
  8. 2026Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systemscited by 0