# Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network

arXiv: https://arxiv.org/abs/1910.09103 · HTML: https://mlsanity.com/paper/1910.09103/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 23 of 23)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks](https://mlsanity.com/paper/2208.05908/) (2022)
- [Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction](https://mlsanity.com/paper/2306.09882/) (2023)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation](https://mlsanity.com/paper/2107.00946/) (2021)
- [EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Prediction](https://mlsanity.com/paper/2303.17027/) (2023)
- [Fairness-enhancing deep learning for ride-hailing demand prediction](https://mlsanity.com/paper/2303.05698/) (2023)
- [Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data](https://mlsanity.com/paper/2108.03900/) (2021)
- [A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing](https://mlsanity.com/paper/2207.04709/) (2022)
- [GNN-based Passenger Request Prediction](https://mlsanity.com/paper/2301.02515/) (2023)
- [ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility](https://mlsanity.com/paper/2306.13897/) (2023)
- [Online Relocating and Matching of Ride-Hailing Services: A Model-Based Modular Approach](https://mlsanity.com/paper/2310.09071/) (2023)
- [Predicting travel demand of a bike sharing system using graph convolutional neural networks](https://mlsanity.com/paper/2408.09317/) (2024)
- [Multi-Graph Inductive Representation Learning for Large-Scale Urban Rail Demand Prediction under Disruptions](https://mlsanity.com/paper/2408.15619/) (2024)
- [Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis](https://mlsanity.com/paper/2411.02542/) (2024)
- [Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions](https://mlsanity.com/paper/2412.03307/) (2024)
- [T-STAR: A Context-Aware Transformer Framework for Short-Term Probabilistic Demand Forecasting in Dock-Based Shared Micro-Mobility](https://mlsanity.com/paper/2602.06866/) (2026)
- [Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN](https://mlsanity.com/paper/2308.16122/) (2023)
- [UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method](https://mlsanity.com/paper/2409.04942/) (2024)
- [CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking](https://mlsanity.com/paper/2412.01419/) (2024)
- [Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing](https://mlsanity.com/paper/2503.24237/) (2025)
- [AMBIT: Augmenting Mobility Baselines with Interpretable Trees](https://mlsanity.com/paper/2512.22466/) (2025)
- [Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction](https://mlsanity.com/paper/2606.21022/) (2026)

## Abstract (arXiv)

With the rapid development of mobile-internet technologies, on-demand ride-sourcing services have become increasingly popular and largely reshaped the way people travel. Demand prediction is one of the most fundamental components in supply-demand management systems of ride-sourcing platforms. With accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations and ride-sharing vehicle routing, etc. Compared to zone-based demand prediction that has been examined by many previous studies, OD-based demand prediction is more challenging. This is mainly due to the complicated spatial and temporal dependencies among demand of different OD pairs. To overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning model for predicting ride-sourcing demand of various OD pairs. Firstly, the model constructs OD graphs, which utilize adjacent matrices to characterize the non-Euclidean pair-wise geographical and semantic correlations among different OD pairs. Secondly, based on the constructed graphs, a residual multi-graph convolutional (RMGC) network is designed to encode the contextual-aware spatial dependencies, and a long-short term memory (LSTM) network is used to encode the temporal dependencies, into a dense vector space. Finally, we reuse the RMGC networks to decode the compressed vector back to OD graphs and predict the future OD demand. Through extensive experiments on the for-hire-vehicles datasets in Manhattan, New York City, we show that our proposed deep learning framework outperforms the state-of-arts by a significant margin.