Modelling Pollution from Traffic, Using Smartphone Data and Python

Explore a PhD project on modeling transportation-related pollution in this EuroPython 2017 conference talk. Discover how Python and smartphone data are utilized to convert sensor information into pollution concentrations in urban settings. Learn about traffic modeling techniques to simulate missing data, locate congestion, and estimate pollution levels. Gain insights into route choice analysis, traffic assignment, econometric models, and congestion modeling. Understand concepts such as Dynamic User Equilibrium (DUE) and Stochastic User Equilibrium (SUE) in transportation systems. Examine a case study of Istanbul and its database implementation. Delve into the importance of monitoring transport system flow and congestion for addressing health effects of local pollution hotspots and designing climate mitigation actions.

Intro
Motivation
Introduction
Basics
Route choice
Artificial example
Routes for N=3
Number of simple routes
Traffic assignment
Econometric model
Cost of travel
Congestion modeling
Equilibrium
Combination of DUE and SUE
Restricted Stochastic User Equlibrium
Case: Istanbul
Database implementation