pabloguarda / mate

Network-wide estimation of traffic flow and travel time with data-driven macroscopic models

Macroscopic Traffic Estimation (MaTE)

MaTE is a data-driven macroscopic model for estimating traffic flow and travel times across the entire transportation networks. MaTE leverages large-scale traffic data to offer accurate, wide-range predictions, making it ideal for urban planning, traffic management, and transportation analysis. This problem is critical in transportation planning applications where the sensor coverage is low and the planned interventions have network-wide impacts.

MaTE is trained using automatic differentiation tools from Tensorflow. It also incorporates a neural network in one of the layers to enrich the mapping of traffic flows into travel times, namely, the performance functions. The input data consists of link characteristics, such as the maximum capacity (flow/hour) and the free flow speed or travel time. The training data are primarily traffic counts and travel time measurements collected at the link level and different hourly periods, which do not need to be available in every link and time period.

The following two diagrams summarize our modeling framework:

Model architecture

Validation and model selection strategy

Preprint

To understand the theory behind the algorithms and the use cases of this codebase, you can review the preprint at http://arxiv.org/abs/2401.17095. Please cite this work as:

@misc{GuardaQian2024macroscopic,
      title={Traffic estimation in unobserved network locations using data-driven macroscopic models}, 
      author={Pablo Guarda and Sean Qian},
      year={2024},
      eprint={2401.17095},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Development Setup

1. Clone this repository.
2. Download and install Anaconda: https://docs.anaconda.com/anaconda/install/index.html
3. Create virtual environment and install dependencies: conda create -n mate
4. Activate environment: conda activate mate
5. Install dependencies: conda env update -f env/cpu-intel.yml. Replace with env/cpu-linux.yml for linux based systems. | You can also setup the enviroment with the yml files env/cpu-silicon.yml or | env/gpu-silicon.yml to train models with cpu and gpu in Apple Silicon, respectively.

Examples

The folder examples/notebooks contains Jupyter notebooks with code demonstrations that can be reproduced from your local environment. If you are using VS Code, please make sure to select the mate environment as your kernel to run each notebook.

Visualizations

Experiments

Model training

Performance functions

Origin-destination demand estimation

Large-scale deployment

Model training

Performance functions

Parameter estimation

Prediction of network-wide traffic congestion

Collaboration

For any questions or interest in collaborating on this project, please open an issue in this repository. This package was developed under the guidance of Prof. Sean Qian.

Funding

This project was partially funded through National Science Foundation Grant CMMI-1751448: Probabilistic Network Flow Theory

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