Modeling and forecasting traffic flows with mobile phone big data in flooding risk areas to support a data-driven decision making

Floods are one of the natural disasters which cause the worst human, social and economic impacts to the detriment of both public and private sectors. Today, public decision-makers can take advantage of the availability of data-driven systems that allow to monitor hydrogeological risk areas and that can be used for predictive purposes to deal with future emergency situations. Flooding risk exposure maps traditionally assume amount of presences constant over time, although crowding is a highly dynamic process in metropolitan areas. Real-time monitoring and forecasting of people’s presences and mobility is thus a relevant aspect for metropolitan areas subjected to flooding risk. In this respect, mobile phone network data have been used with the aim of obtaining dynamic measure for the exposure risk in areas with hydrogeological criticality. In this work, we use mobile phone origin-destination signals on traffic flows by Telecom Italia Mobile (TIM) users with the aim of forecasting the exposure risk and thus to help decision-makers in warning to who is transiting through that area. To model the complex seasonality of traffic flows data, we adopt a novel methodological strategy based on introducing in a Vector AutoRegressive with eXogenous variable (VARX) model a Dynamic Harmonic Regression (DHR) component. We apply the method to the case study of the “Mandolossa”, an urbanized area subject to flooding located on the western outskirt of Brescia, using hourly-basis data from September 2020 to August 2021. A cross validation based on the hit-rate and the mean absolute percentage error measures show a good forecasting accuracy.