# Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

arXiv: https://arxiv.org/abs/2004.08038 · HTML: https://mlsanity.com/paper/2004.08038/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

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

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited](https://mlsanity.com/paper/2202.03580/) (2022)
- [Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning](https://mlsanity.com/paper/2101.00554/) (2021)
- [Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities](https://mlsanity.com/paper/2202.03630/) (2022)
- [Graph Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2310.15978/) (2023)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction](https://mlsanity.com/paper/2307.00495/) (2023)
- [Traffic Prediction with Transfer Learning: A Mutual Information-based Approach](https://mlsanity.com/paper/2303.07184/) (2023)
- [Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention](https://mlsanity.com/paper/2207.00824/) (2022)
- [Transfer Learning for Node Regression Applied to Spreading Prediction](https://mlsanity.com/paper/2104.00088/) (2021)
- [Structured Time Series Prediction without Structural Prior](https://mlsanity.com/paper/2202.03539/) (2022)
- [Graph Pruning Based Spatial and Temporal Graph Convolutional Network with Transfer Learning for Traffic Prediction](https://mlsanity.com/paper/2409.16532/) (2024)
- [TLETA: Deep Transfer Learning and Integrated Cellular Knowledge for Estimated Time of Arrival Prediction](https://mlsanity.com/paper/2206.08513/) (2022)
- [Newell's theory based feature transformations for spatio-temporal traffic prediction](https://mlsanity.com/paper/2307.05949/) (2023)
- [A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios](https://mlsanity.com/paper/2308.15464/) (2023)
- [FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning](https://mlsanity.com/paper/2405.08843/) (2024)
- [Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracy](https://mlsanity.com/paper/2406.06645/) (2024)
- [Spatio-Temporal Partial Sensing Forecast for Long-term Traffic](https://mlsanity.com/paper/2408.02689/) (2024)
- [FedCross: Intertemporal Federated Learning Under Evolutionary Games](https://mlsanity.com/paper/2412.16968/) (2024)
- [Pruning for Generalization: A Transfer-Oriented Spatiotemporal Graph Framework](https://mlsanity.com/paper/2602.04153/) (2026)

## Abstract (arXiv)

Highway traffic modeling and forecasting approaches are critical for intelligent transportation systems. Recently, deep-learning-based traffic forecasting methods have emerged as state of the art for a wide range of traffic forecasting tasks. However, these methods require a large amount of training data, which needs to be collected over a significant period of time. This can present a number of challenges for the development and deployment of data-driven learning methods for highway networks that suffer from lack of historical data. A promising approach to address this issue is transfer learning, where a model trained on one part of the highway network can be adapted for a different part of the highway network. We focus on diffusion convolutional recurrent neural network (DCRNN), a state-of-the-art graph neural network for highway network forecasting. It models the complex spatial and temporal dynamics of the highway network using a graph-based diffusion convolution operation within a recurrent neural network. DCRNN cannot perform transfer learning, however, because it learns location-specific traffic patterns, which cannot be used for unseen regions of the network. To that end, we develop a new transfer learning approach for DCRNN, where a single model trained on data-rich regions of the highway network can be used to forecast traffic on unseen regions of the highway network. We evaluate the ability of our approach to forecast the traffic on the entire California highway network with one year of time series data. We show that TL-DCRNN can learn from several regions of the California highway network and forecast the traffic on the unseen regions of the network with high accuracy. Moreover, we demonstrate that TL-DCRNN can learn from San Francisco region traffic data and can forecast traffic on the Los Angeles region and vice versa.