# Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter

arXiv: https://arxiv.org/abs/1905.00406 · HTML: https://mlsanity.com/paper/1905.00406/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2019.

## Built on by (most cited first, 13 of 13)

- [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)
- [End-to-End Heterogeneous Graph Neural Networks for Traffic Assignment](https://mlsanity.com/paper/2310.13193/) (2023)
- [Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks](https://mlsanity.com/paper/2303.04040/) (2023)
- [On The Temporal Domain of Differential Equation Inspired Graph Neural Networks](https://mlsanity.com/paper/2401.11074/) (2024)
- [Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction](https://mlsanity.com/paper/2206.15005/) (2022)
- [Traffic Prediction with Transfer Learning: A Mutual Information-based Approach](https://mlsanity.com/paper/2303.07184/) (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 DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence](https://mlsanity.com/paper/2307.05623/) (2023)
- [Large-Scale OD Matrix Estimation with A Deep Learning Method](https://mlsanity.com/paper/2310.05753/) (2023)
- [Dyna-Style Learning with A Macroscopic Model for Vehicle Platooning in Mixed-Autonomy Traffic](https://mlsanity.com/paper/2405.02062/) (2024)
- [Structured Adaptive Tensor Prediction for Streaming Data](https://mlsanity.com/paper/2606.10085/) (2026)

## Abstract (arXiv)

Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic Origin-Destination (O-D) demand matrices is that demands cannot be directly measured by traffic sensors; instead, they have to be inferred from aggregate traffic flow data on traffic links. Specifically, spatial correlation, congestion and time dependent factors need to be considered in general transportation networks. In this paper we propose a novel O-D prediction framework combining heterogeneous prediction in graph neural networks and Kalman filter to recognize spatial and temporal patterns simultaneously. The underlying road network topology is converted into a corresponding line graph in the newly designed Fusion Line Graph Convolutional Networks (FL-GCNs), which provide a general framework of predicting spatial-temporal O-D flows from link information. Data from New Jersey Turnpike network are used to evaluate the proposed model. The results show that our proposed approach yields the best performance under various prediction scenarios. In addition, the advantage of combining deep neural networks and Kalman filter is demonstrated.