# Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation

arXiv: https://arxiv.org/abs/2404.07446 · HTML: https://mlsanity.com/paper/2404.07446/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network](https://mlsanity.com/paper/2104.12518/) (2021)
- [A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism](https://mlsanity.com/paper/2205.04762/) (2022)
- [Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs](https://mlsanity.com/paper/1904.08831/) (2019)
- [Traffic Queue Length and Pressure Estimation for Road Networks with Geometric Deep Learning Algorithms](https://mlsanity.com/paper/1905.03889/) (2019)

## Built on by (most cited first, 3 of 3)

- [MTDT: A Multi-Task Deep Learning Digital Twin](https://mlsanity.com/paper/2405.00922/) (2024)
- [Dynamic Graph Attention Networks for Travel Time Distribution Prediction in Urban Arterial Roads](https://mlsanity.com/paper/2412.11095/) (2024)
- [TGDT: A Temporal Graph-based Digital Twin for Urban Traffic Corridors](https://mlsanity.com/paper/2504.18008/) (2025)

## Abstract (arXiv)

Traffic congestion has significant economic, environmental, and social ramifications. Intersection traffic flow dynamics are influenced by numerous factors. While microscopic traffic simulators are valuable tools, they are computationally intensive and challenging to calibrate. Moreover, existing machine-learning approaches struggle to provide lane-specific waveforms or adapt to intersection topology and traffic patterns. In this study, we propose two efficient and accurate "Digital Twin" models for intersections, leveraging Graph Attention Neural Networks (GAT). These attentional graph auto-encoder digital twins capture temporal, spatial, and contextual aspects of traffic within intersections, incorporating various influential factors such as high-resolution loop detector waveforms, signal state records, driving behaviors, and turning-movement counts. Trained on diverse counterfactual scenarios across multiple intersections, our models generalize well, enabling the estimation of detailed traffic waveforms for any intersection approach and exit lanes. Multi-scale error metrics demonstrate that our models perform comparably to microsimulations. The primary application of our study lies in traffic signal optimization, a pivotal area in transportation systems research. These lightweight digital twins can seamlessly integrate into corridor and network signal timing optimization frameworks. Furthermore, our study's applications extend to lane reconfiguration, driving behavior analysis, and facilitating informed decisions regarding intersection safety and efficiency enhancements. A promising avenue for future research involves extending this approach to urban freeway corridors and integrating it with measures of effectiveness metrics.