# Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurations

arXiv: https://arxiv.org/abs/2405.14257 · HTML: https://mlsanity.com/paper/2405.14257/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks](https://mlsanity.com/paper/1705.02699/) (2017)
- [Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN](https://mlsanity.com/paper/1903.12549/) (2019)
- [Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control](https://mlsanity.com/paper/2210.10453/) (2022)
- [A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure](https://mlsanity.com/paper/2105.02572/) (2021)
- [Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction](https://mlsanity.com/paper/1812.07151/) (2018)
- [Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics](https://mlsanity.com/paper/2209.05726/) (2022)
- [On the inefficiency of ride-sourcing services towards urban congestion](https://mlsanity.com/paper/2007.00980/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning to Learn the Macroscopic Fundamental Diagram using Physics-Informed and meta Machine Learning techniques](https://mlsanity.com/paper/2508.14137/) (2025)

## Abstract (arXiv)

In large-scale traffic optimization, models based on Macroscopic Fundamental Diagram (MFD) are recognized for their efficiency in broad network analyses. However, they fail to reflect variations in the individual traffic status of each road link, leading to a gap in detailed traffic optimization and analysis. To address the limitation, this study introduces a Local Correction Factor (LCF) that represents local speed deviations between the actual link speed and the MFD average speed based on the link configuration. The LCF is calculated using a deep learning function that takes as inputs the average speed from the MFD and the road network configuration. Our framework integrates Graph Attention Networks (GATs) with Gated Recurrent Units (GRUs) to capture both the spatial configurations and temporal correlations within the network. Coupled with a strategic network partitioning method, our model enhances the precision of link-level traffic speed estimations while preserving the computational advantages of aggregate models. In our experiments, we evaluate the proposed LCF across various urban traffic scenarios, including different levels of origin-destination trip demand and distribution, as well as diverse road configurations. The results demonstrate the robust adaptability and effectiveness of the proposed model. Furthermore, we validate the practicality of our model by calculating the travel time of each randomly generated path, achieving an average error reduction of approximately 84% relative to MFD-based results.