Digital Twin for urban car traffic emission: A case study in Kista, Stockholm

There are three parts in the replication package:

Nowcasting

The Nowcasting folder contains the complete pipeline to train the CNN model (Python Code) with images of different vehicle classes and subsequently use the trained model to classify vehicles from video data and estimate their emissions. 

ODME

The ODME folder includes the software DTALite as well as its input data for estimating OD demand. 

Simulation

The simulation folder contains the raw demand data extracted from Dynameq initially calibrated by City of Stockholm for the entire Stockholm area and the scripts to transform the demand first into MATSim format and subsequently into SUMO format to realize the hybrid simulation format. Ultimately following this pipeline, the emission for the study area in Kista can be realized for given demand of the current scenario and alternative future scenarios.