mlsanityData to 7 Oct 2026

Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing

arXiv 2503.24237 ↗First posted 31 Mar 2025cs.LG cs.AI stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

Accurate spatial-temporal prediction of network-based travelers' requests is crucial for the effective policy design of ridesharing platforms. Having knowledge of the total demand between various locations in the upcoming time slots enables platforms to proactively prepare adequate supplies, thereby increasing the likelihood of fulfilling travelers' requests and redistributing idle drivers to areas with high potential demand to optimize the global supply-demand equilibrium. This paper delves into the prediction of Origin-Destination (OD) demands at a fine-grained spatial level, especially when confronted with an expansive set of local regions. While this task holds immense practical value, it remains relatively unexplored within the research community. To fill this gap, we introduce a novel prediction model called OD-CED, which comprises an unsupervised space coarsening technique to alleviate data sparsity and an encoder-decoder architecture to capture both semantic and geographic dependencies. Through practical experimentation, OD-CED has demonstrated remarkable results. It achieved an impressive reduction of up to 45% reduction in root-mean-square error and 60% in weighted mean absolute percentage error over traditional statistical methods when dealing with OD matrices exhibiting a sparsity exceeding 90%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

17 of 44 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
  4. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  5. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  6. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  7. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  8. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
  9. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  10. 2021Do Transformer Modifications Transfer Across Implementations and Applications?cited by 87
  11. 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Predictioncited by 67
  12. 2018Bike Flow Prediction with Multi-Graph Convolutional Networkscited by 42
5 more
  1. 2019Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Predictioncited by 34
  2. 2022Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networkscited by 29
  3. 2019Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional networkcited by 23
  4. 2022Statistical Deep Learning for Spatial and Spatio-Temporal Datacited by 15
  5. 2019Statistical inference of probabilistic origin-destination demand using day-to-day traffic datacited by 4

Built on by

Not cited yet.