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**OUTFIT - Dynamic Road Noise Maps Based on Crowdsourced Data and Represented in Digital Twins**
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The OUTFIT PRIN 2022 project aims to dynamically represent road traffic noise (RTN) in a Digital Twin (DT) model by optimizing the data flow related to noise levels and citizen perception.
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The **OUTFIT PRIN 2022** project aims to dynamically represent road traffic noise (RTN) in a **Digital Twin (DT)** model by optimizing the data flow related to noise levels and citizen perception.
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## Overview
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This repository is dedicated to developing the pipeline that, starting from raw data obtained via API calls to the Google Maps Directions service, provides an output matrix of the perceived sound power at receivers located on building facades in the area of interest.
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This repository provides tools to:
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- Collect raw data via API calls to the **Google Maps Directions** service.
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- Process this data to generate **perceived sound power** at receivers located on building facades in the area of interest.
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# Installation
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The project needs the following package installed:
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## Requirements
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This project requires the following Python packages:
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- python-dotenv
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- requests
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- numpy
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- pyarrow
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- pandas
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- wxpython
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- pyinstaller
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We suggest to install them in a Python environment with `conda` (see [Miniconda Installation](#Miniconda-Installation)) or `venv` (see [Python Venv Installation](Python-Venv-Installation)).
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It is recommended to install these packages inside a Python virtual environment using **conda**or **venv**.
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## Miniconda Installation
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The following commands to install Miniconda3 on your system are taken from the [official documentation](https://docs.anaconda.com/miniconda/).
# Close and reopen your terminal to activate conda and
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# create a new environment from the environment.yml file
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conda env create -f environment.yml
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```
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This will automatically install all required packages defined in `environment.yml`.
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## Python Venv Installation
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The following commands to create a Python virtual environment are taken from the [official documentation](https://docs.python.org/3/library/venv.html).
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Before creating the virtual environment, make sure you have [cloned the repository](#clone-the-repository) and `cd` into it (e.g., `cd ~/OUTFIT`).
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## Using venv
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`venv` is a built-in Python module for creating isolated environments.
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Using a virtual environment ensures that your project dependencies do not conflict with other Python projects on your system.
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Steps to create a `venv`, install packages, and enter the environment:
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