DRIVE v1.0

Data-driven Road-Transport Inventory for Vehicle Emissions

Python-based framework to calculate road traffic emissions in urban areas. The method is based on a multi-modal macroscopic traffic model (static traffic demand model) and data from multiple vehicle-specific traffic counting stations (dynamic traffic data) to estimate hourly traffic volume and traffic condition on a road-link level. This granular activity data is combined with HBEFA 4.2 emission factors to estimate hot vehicle exhaust emissions and cold start excess emissions. In conclusion, this framework provides methods to...

... estimate and generate a high-resolution spatial emission map for road transport emissions.
... generate accurate, data-based temporal profiles for greehouse gases and air pollutants.
... conduct a data-based uncertainty analysis of the activity data and resulting emission estimate.

The project is part of ICOS Cities, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 101037319.

How to use DRIVE

IMPORTANT: Comprehensive data availability is fundamental when using this framework. Please check if you can fulfill all data requirements listed in the data folder first.

IDE Setup

1. Install and activate python in a virtual environment
   python3 -m venv .venv
source .venv/bin/activate
2. Install required packages
   pip install -r requirements.txt
3. Install jupyter kernel for the virtual environment.
   ipython kernel install --user --name=drive-inventory
4. Run a jupyterlab on your computer and select the virtual environment drive-inventory as kernel.
   jupyterlab

Application

When the data requirements are fulfilled and the IDE is running, please refer to the notebooks folder for further instructions.

Related Work

Kühbacher, D., Aigner, P., Super, I., Droste, A., Denier van der Gon, H., Ilic, M., and Chen, J.: Bottom-up estimation of traffic emissions in Munich based on macroscopic traffic simulation and counting data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12997, https://doi.org/10.5194/egusphere-egu23-12997, 2023.

Contributors

Daniel Kühbacher (Lead) @DanielKuebi
Ali Ahmand Khan @alimayo
Julian Bärtschi @jbaertschi