# Learning Traffic Speed Dynamics from Visualizations

arXiv: https://arxiv.org/abs/2105.01423 · HTML: https://mlsanity.com/paper/2105.01423/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 8 of 8)

- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning](https://mlsanity.com/paper/2001.02492/) (2020)
- [Learning-based Traffic State Reconstruction using Probe Vehicles](https://mlsanity.com/paper/2011.05031/) (2020)
- [Traffic Data Imputation using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2002.04406/) (2020)
- [Multi-scale Perimeter Control Approach in a Connected-Vehicle Environment](https://mlsanity.com/paper/1612.05910/) (2016)
- [Learning Traffic Flow Dynamics using Random Fields](https://mlsanity.com/paper/1806.08764/) (2018)
- [Position weighted backpressure intersection control for urban networks](https://mlsanity.com/paper/1810.11406/) (2018)
- [Sparse Travel Time Estimation from Streaming Data](https://mlsanity.com/paper/1804.08130/) (2018)

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

- [Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems](https://mlsanity.com/paper/2308.07051/) (2023)
- [Urban traffic analysis and forecasting through shared Koopman eigenmodes](https://mlsanity.com/paper/2409.04728/) (2024)
- [Generalized adaptive smoothing based neural network architecture for traffic state estimation](https://mlsanity.com/paper/2301.03439/) (2023)
- [Automated Construction of Time-Space Diagrams for Traffic Analysis Using Street-View Video Sequence](https://mlsanity.com/paper/2308.06098/) (2023)

## Abstract (arXiv)

Space-time visualizations of macroscopic or microscopic traffic variables is a qualitative tool used by traffic engineers to understand and analyze different aspects of road traffic dynamics. We present a deep learning method to learn the macroscopic traffic speed dynamics from these space-time visualizations, and demonstrate its application in the framework of traffic state estimation. Compared to existing estimation approaches, our approach allows a finer estimation resolution, eliminates the dependence on the initial conditions, and is agnostic to external factors such as traffic demand, road inhomogeneities and driving behaviors. Our model respects causality in traffic dynamics, which improves the robustness of estimation. We present the high-resolution traffic speed fields estimated for several freeway sections using the data obtained from the Next Generation Simulation Program (NGSIM) and German Highway (HighD) datasets. We further demonstrate the quality and utility of the estimation by inferring vehicle trajectories from the estimated speed fields, and discuss the benefits of deep neural network models in approximating the traffic dynamics.