Mobility data mining with Python (TDAMDMP)

The sharply increasing amount of data available through the Internet has deeply modified the way how transport systems are modelled and analysed. This new paradigm brings plenty of opportunities for users and organisations, but it also creates novel challenges to address. This project provides a case study about collecting real world mobility data available through the web and analyse them using the Python programming language.

Audience (and prerequisites)

Anyone with a basic knowledge in programming who are interested in developing Python tools for the transport sector

Approaches (Objective)

In the first part of the project, students will collect and properly organise different types of mobility-related open data. Then, students will characterise the different datasets using visualisations and unsupervised techniques.

Finally, once the data are well-structured, students will develop algorithms to forecast mobility demand in the dimensions of space and time.