This exploratory tells stories about cities and people living in it. Data scientists describe those territories by means of data, statistics and models. This allows citizens and local administrator to better understand cities and how to improve them.
How do people move into the city? How does the traffic change during the day? And how does it vary during the week? How does the turism presence affect the traffic? Our data scientits already study the traffic in the Italian cities of Pisa and Florence by analyzing Big Data sources such as mobile phone traces, veicular gps and social media data as proxy of human behaviour.
The results could be useful for both local administrators and citizens. The local administrators could have a tool to quantify accurately city’s traffic and understand city’s usage, so they could take better decisions to manage mobility. Citizens could take informations to know traffic situation in real time and they could choose the best and fastest way.
Our studies could be useful in carpooling, too. Indeed, Big Data analysis can suggest to citizens who can share the travel with them.
Geo-localized Twitter Data - Tuscany
|Method||Partner||SoBigData RI - Integration|
|Urban Profiles||AALTO||Web page|
|Urban Mobility Atlas||CNR||Web page, Web service|
|Trajectory Builder||CNR||Service hosted, Download|
|Trip Builder||CNR||Web page|
|O/D Matrix||CNR||Web service (in Urban Mobility Atlas)|
|Exploration of Time||FRH||Download|
|Statistical Validation||SNS||Service hosted, Download|
Michael Mathioudakis - AALTO
Salvo Rinzivillo - CNR
Lorenzo Gabrielli - CNR
Roberto Trasarti - CNR
Cristina Muntean - CNR
Riccardo Guidotti - CNR
Francesca Pratesi - UNIPI
Daniele Regoli - SNS
Gennady Andrienko - FHR
Natalia Andrienko - FHR