# Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction

arXiv: https://arxiv.org/abs/2205.14593 · HTML: https://mlsanity.com/paper/2205.14593/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 29 May 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://mlsanity.com/paper/1811.02146/) (2018)
- [Node Embedding over Temporal Graphs](https://mlsanity.com/paper/1903.08889/) (2019)

## Built on by (most cited first, 4 of 4)

- [FusionTransNet for Smart Urban Mobility: Spatiotemporal Traffic Forecasting Through Multimodal Network Integration](https://mlsanity.com/paper/2405.05786/) (2024)
- [Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction](https://mlsanity.com/paper/2408.14762/) (2024)
- [DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling](https://mlsanity.com/paper/2606.09086/) (2026)
- [Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction](https://mlsanity.com/paper/2606.21022/) (2026)

## Abstract (arXiv)

Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a challenging problem since the number of OD pairs is usually quadratic to the number of stations. In this case, most of the existing spatiotemporal methods fail to handle spatial relations on such a large scale. To address this problem, this paper provides a dynamic graph representation learning framework for OD demands prediction. In particular, a hierarchical memory updater is first proposed to maintain a time-aware representation for each node, and the representations are updated according to the most recently observed OD trips in continuous-time and multiple discrete-time ways. Second, a spatiotemporal propagation mechanism is provided to aggregate representations of neighbor nodes along a random spatiotemporal route which treats origin and destination as two different semantic entities. Last, an objective function is designed to derive the future OD demands according to the most recent node representations, and also to tackle the data sparsity problem in OD prediction. Extensive experiments have been conducted on two real-world datasets, and the experimental results demonstrate the superiority of the proposed method. The code and data are available at https://github.com/Rising0321/HMOD.