SIGNUM

The availability of big data drawn from mobile phone data is becoming increasingly essential in many fields of study that pertain to the analysis of social, economic and environmental aspects, since they allow a dynamic and fine-grained representation of human activities. In this context, recently developed technology has been able to capture signals produced by many kinds of devices and transmit them from/to terminal devices through global positioning systems (GPS). Depending on the purpose, one can then retrieve useful insights related to smart and sustainable mobility.

The 24-months SIGNUM PRIN/PNRR project, funded by the Italian Ministry of Education, Universities and Research, aims at:

• Developing statistical methods to estimate and predict the amount of traffic during specific time intervals in specific areas of interest using mobile phone data.

• Investigating the relation between mobility, pollution and natural disasters by matching the existing indices on air quality and pollutant emissions with the estimated traffic data obtained from the application of the developed methods. 

In pursuing the above targets, the primary results that this project intends to offer are as follows: 

1. Developing robust spatiotemporal statistical methods to accurately estimate and predict traffic at the “small” area level by combining all sources of mobile phone data. 

2. Matching predicted traffic data obtained from the application of the developed methods with existing indices on air quality and pollutant emissions by using specific spatial and geo-statistical aggregation and interpolation methods. An ancillary result is the production of a spatiotemporal dataset for traffic in Lombardia. 

3. Studying the relation between air quality, pollutant emissions and traffic by conducting a counterfactual analysis aimed at investigating the role of pollution policies. 

4. Studying the relation between traffic and natural disasters from a prevention perspective. The importance of these goals is evident due to the increasing interest in the nexus between mobility and the environment. Moreover, the application of robust methods to innovative data is another valuable contribution of this project.

The idea of this project is based on the availability (for Lombardia) of different types of mobile phone data: 

• “Erlang” data (average number of mobile phones assigned to a cell within a specific time interval), representing the measure for “density” of people’s presence in a specific area; 

• Minimisation drive test (MDT) data (phone calls, text messages, internet browsing signals transmitted over the 3G/4G mobile network from/to GPS enabled terminal devices), which is interpreted similar to Erlang data but configured as “marked point patterns” that present lower measurement errors; and 

• Origin-destination (OD) data, representing the number of signals that are first retrieved in a specific area and then in another specific area in a specific time interval—meant to be considered traffic data