This repository contains the code used to run the sensitivity analysis in the paper "A Global Sensitivity Analysis of Traffic Microsimulation Input Parameters on Performance Metrics" by Maxwell Schrader and Joshua Bittle.

1. Figure 1: Not included in this repository
2. Figure 2: Not included in this repository
3. Figure 3: ./notebooks/SA1/NSweep.ipynb
4. Figure 4: ./notebooks/SA1/SA1.ipynb
5. Figure 5: ./notebooks/SA1/SA1.ipynb
6. Figure 6: ./notebooks/SA1/SA1.ipynb
7. Figure 7: ./notebooks/SA2/SA2.ipynb
8. Figure 8: ./notebooks/SA2/SA2.ipynb

1. Create a virtual environment: python3 -m venv venv
2. Activate the virtual environment: source venv/bin/activate
3. Install the requirements: pip install -r requirements.txt

1. Install SUMO and add the bin directory to your PATH environment variable.
   • You will also need to set the SUMO_HOME environment variable to the path of your SUMO installation.
2. Some of the scrips in ./scripts require the jq command line tool. Install it using your package manager of choice.

.
├── configs
│   ├── common_blocks
│   └── parameter_sets
│       ├── literature
│       └── paper
├── data
│   ├── LiteratureDefaults
│   │   ├── ...
│   ├── SA1
│   │   ├── ...
│   └── SA2
│       └── ...
├── notebooks
│   ├── SA1
│   └── SA2
├── scripts
├── simulation
│   ├── additional
│   │   ├── detectors
│   │   ├── polygons
│   │   └── signals
│   ├── network
│   └── routes
└── src
    ├── plotting
    └── sa_helpers

All sensitivity analyses are ran using the sumo-pipe command from the sumo-pipelines python library.

They use Ray to parallelize the simulations. The number of parallel simulations can be configured using the ray start command:

ray start --head --port=6379 --num-cpus=<desired cpu num>"

All simulations rely on three environment variables:

1. SUMO_HOME: The path to your SUMO installation
2. PYTHONPATH: The path to ./src must be added to the PYTHONPATH environment variable.
3. PROJECT_ROOT: The path to the root of this project. This is used to find the simulation data & set the output

The sensitivity analyis is configured by three YAML files:

1. ./configs/sa1.yaml
   • Defines the workflow and the Metadata
2. ./configs/common_blocks/blocks.yaml
   • Defines the blocks used in the workflow
3. ./configs/parameter_sets/paper/sa1.yaml
   • Definies the Sensitivity Analysis parameters & vehicle distributions

The analysis can be run using the following command:

export PYTHONPATH="$PYTHONPATH:$PWD";
export PROJECT_ROOT="$PWD";
sumo-pipe ./configs/sa1.yaml ./configs/common_blocks/blocks.yaml ./configs/parameter_sets/paper/sa1.yaml

The results of the analysis will be stored according to the Metadata.output_dir parameter in the ./config/sa1.yaml file. The reults must be first processed using scripts/process-results.py before they can be analyzed.

python scripts/process_results.py <path to results directory>

You can batch execute this for many simulations with

for dir in <path to results directory>/**/*; do
    python scripts/process_results.py $dir
done

The process results script may take a while to run depending on the number of simulations.

Like SA 1, the sensitivity analyis is configured by three YAML files:

1. ./configs/sa2.yaml
   • Defines the workflow and the Metadata
2. ./configs/common_blocks/blocks.yaml
   • Defines the blocks used in the workflow
3. ./configs/parameter_sets/paper/sa2.yaml
   • Definies the Sensitivity Analysis parameters & vehicle distributions

The analysis can be run using the following command:

export PYTHONPATH="$PYTHONPATH:$PWD";
export PROJECT_ROOT="$PWD";
sumo-pipe ./configs/sa2.yaml ./configs/common_blocks/blocks.yaml ./configs/parameter_sets/paper/sa2.yaml

The results of the analysis will be stored according to the Metadata.output_dir parameter in the ./config/sa2.yaml file. The reults must be first processed using scripts/process-results.py before they can be analyzed.

@ARTICLE{10462521,
  author={Schrader, Maxwell and Bittle, Joshua},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={A Global Sensitivity Analysis of Traffic Microsimulation Input Parameters on Performance Metrics}, 
  year={2024},
  volume={},
  number={},
  pages={1-14},
  doi={10.1109/TITS.2024.3372334}}