TY - JOUR T1 - Generating mobility networks with generative adversarial networks JF - EPJ data science Y1 - 2022 A1 - Giovanni Mauro A1 - Luca, Massimiliano A1 - Longa, Antonio A1 - Lepri, Bruno A1 - Luca Pappalardo AB - The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city’s entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people’s movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis. VL - 11 ER - TY - JOUR T1 - Give more data, awareness and control to individual citizens, and they will help COVID-19 containment Y1 - 2021 A1 - Mirco Nanni A1 - Andrienko, Gennady A1 - Barabasi, Albert-Laszlo A1 - Boldrini, Chiara A1 - Bonchi, Francesco A1 - Cattuto, Ciro A1 - Chiaromonte, Francesca A1 - Comandé, Giovanni A1 - Conti, Marco A1 - Coté, Mark A1 - Dignum, Frank A1 - Dignum, Virginia A1 - Domingo-Ferrer, Josep A1 - Ferragina, Paolo A1 - Fosca Giannotti A1 - Riccardo Guidotti A1 - Helbing, Dirk A1 - Kaski, Kimmo A1 - Kertész, János A1 - Lehmann, Sune A1 - Lepri, Bruno A1 - Lukowicz, Paul A1 - Matwin, Stan A1 - Jiménez, David Megías A1 - Anna Monreale A1 - Morik, Katharina A1 - Oliver, Nuria A1 - Passarella, Andrea A1 - Passerini, Andrea A1 - Dino Pedreschi A1 - Pentland, Alex A1 - Pianesi, Fabio A1 - Francesca Pratesi A1 - S Rinzivillo A1 - Salvatore Ruggieri A1 - Siebes, Arno A1 - Torra, Vicenc A1 - Roberto Trasarti A1 - Hoven, Jeroen van den A1 - Vespignani, Alessandro AB - The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. SN - 1572-8439 UR - https://link.springer.com/article/10.1007/s10676-020-09572-w JO - Ethics and Information Technology ER - TY - CONF T1 - Weak nodes detection in urban transport systems: Planning for resilience in Singapore T2 - 2018 IEEE 5th international conference on data science and advanced analytics (DSAA) Y1 - 2018 A1 - Ferretti, Michele A1 - Barlacchi, Gianni A1 - Luca Pappalardo A1 - Lucchini, Lorenzo A1 - Lepri, Bruno AB - The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc. . . ). In this perspective, we propose ACHILLES, an application to models people's movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention. JF - 2018 IEEE 5th international conference on data science and advanced analytics (DSAA) PB - IEEE UR - https://ieeexplore.ieee.org/abstract/document/8631413/authors#authors ER -