Modelling pollution from traffic, using Smartphone data and Python

The talk presents results from my PhD project on models for transportation related pollution.

Pollution from personal transport in Cities is a big and growing problem. By monitoring the flow, and congestion in the transport system two goals can be achieved. First, the adherence to agreed limit values (or breaking said limits) can be followed and used to decrease health effects of local pollution hotspots. Secondly, monitoring of the total emission of climate forcing gases from transportation, is important for designing climate mitigation actions.

Python is used in combination with other tools to convert sensor data from smartphones, into pollution concentrations in urban settings. To mitigate the lack of complete data coverage, the missing data is simulated by a traffic model, to locate congestion and model the traffic related pollution concentration.