Mobility Data Analysis

Big Data Mining
Academic Year: 

The purpose of the course is to introduce the main analysis techniques for spatio-temporal data, with a particular focus on human mobility (including vehicles), aimed to better understand the overall mobility of a territory. The presentation will be supported by several case studies developed with the laboratory.


Mobility data types; spatio-temporal trajectories; routines and mobility profiles; trajectory patterns and trajectory models.

Technics and tools: 

The methods and techniques presented include the following: trajectory and profile reconstruction methods; O/D matrices of the mobility demand; trajectory clustering; flock-, swarm- and convoy- patterns; mobile activity recognition. In the course, the M-Atlas platform will be introduced, for the analysis of mobile objects' trajectories.

Case studies and datasets: 

Sample case studies will be presented, on: study of urban mobility through GPS data; study of presence and inter-city fluxes through mobile phone data (application of Big Data for Official Statistics); traffic planning on national scale through mobile phone data; public transport studies (performances and integration with carpooling) based on GPS and planned public transport service. Exercises will involve (also) public GPS datasets (taxi) and/or social media data (Flickr).


Knowledge of the main opportunities, issues and limitations in the analysis of Bog Mobility Data; knowledge of the main data mining methods for such data; skills to use some specific analysis tools, developed over real datasets.