# Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach

arXiv: https://arxiv.org/abs/2011.05602 · HTML: https://mlsanity.com/paper/2011.05602/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks](https://mlsanity.com/paper/2208.05908/) (2022)
- [Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach](https://mlsanity.com/paper/2112.08078/) (2021)
- [Convolutional Learning on Multigraphs](https://mlsanity.com/paper/2209.11354/) (2022)
- [Learning with Multigraph Convolutional Filters](https://mlsanity.com/paper/2210.16272/) (2022)
- [Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method](https://mlsanity.com/paper/2203.00422/) (2022)
- [A multi-functional simulation platform for on-demand ride service operations](https://mlsanity.com/paper/2303.12336/) (2023)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network](https://mlsanity.com/paper/2412.19842/) (2024)
- [Unsupervised Knowledge Adaptation for Passenger Demand Forecasting](https://mlsanity.com/paper/2206.04053/) (2022)
- [Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach](https://mlsanity.com/paper/2312.05576/) (2023)
- [SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensing](https://mlsanity.com/paper/2411.18432/) (2024)
- [Toward an Integrated Cross-Urban Accident Prevention System: A Multi-Task Spatial-Temporal Learning Framework for Urban Safety Management](https://mlsanity.com/paper/2601.05521/) (2026)
- [Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors](https://mlsanity.com/paper/2607.28525/) (2026)
- [SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark](https://mlsanity.com/paper/2610.13730/) (2026)

## Abstract (arXiv)

Ride-hailing platforms generally provide various service options to customers, such as solo ride services, shared ride services, etc. It is generally expected that demands for different service modes are correlated, and the prediction of demand for one service mode can benefit from historical observations of demands for other service modes. Moreover, an accurate joint prediction of demands for multiple service modes can help the platforms better allocate and dispatch vehicle resources. Although there is a large stream of literature on ride-hailing demand predictions for one specific service mode, little efforts have been paid towards joint predictions of ride-hailing demands for multiple service modes. To address this issue, we propose a deep multi-task multi-graph learning approach, which combines two components: (1) multiple multi-graph convolutional (MGC) networks for predicting demands for different service modes, and (2) multi-task learning modules that enable knowledge sharing across multiple MGC networks. More specifically, two multi-task learning structures are established. The first one is the regularized cross-task learning, which builds cross-task connections among the inputs and outputs of multiple MGC networks. The second one is the multi-linear relationship learning, which imposes a prior tensor normal distribution on the weights of various MGC networks. Although there are no concrete bridges between different MGC networks, the weights of these networks are constrained by each other and subject to a common prior distribution. Evaluated with the for-hire-vehicle datasets in Manhattan, we show that our propose approach outperforms the benchmark algorithms in prediction accuracy for different ride-hailing modes.