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DATASIM aims at providing an entirely new and highly detailed spatial-temporal microsimulation methodology for human mobility, grounded on massive amounts of Big data of various types and from various sources, e.g. GPS, mobile phones and social networking sites, with the goal to forecast the nation-wide consequences of a massive switch to electric vehicles, given the intertwined nature of mobility and power distribution networks. Significant breakthroughs can be achieved from this project, contributing to the milestones that were set forward in the European Industry Roadmap for the Electrification of Road Transport from today till 2020. Many scientists have already pointed out that the goal of social sciences is not simply to understand how people behave in large groups, but to understand what motivates individuals to behave the way they do. This fundamental insight, which can be gained from this project, is a step forward towards the solution of this important challenge; it can help us to better understand the dynamics of our society and, in the longer run, to have an impact on overall and wider societal well-being.


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Gabbrielli, M., B. Furletti, F. Giannotti, M. Nanni, and S. Rinzivillo, "Use of Mobile Phone Data to Estimate Visitors Mobility Flows", Software Engineering and Formal Methods, vol. 8938, no. Lecture Notes in Computer Science: Springer International Publishing, pp. 214-226, 2015.
de Lira, V M., V C. Times, C. Renso, and S. Rinzivillo, "ComeWithMe: An Activity-Oriented Carpooling Approach", 2015 {IEEE} 18th International Conference on Intelligent Transportation Systems: Institute of Electrical {&} Electronics Engineers ({IEEE}), 09/2015.
Nanni, M., R. Trasarti, P. Cintia, B. Furletti, C. Renso, M. Gabbrielli, S. Rinzivillo, and F. Giannotti, "Mobility Profiling", Data Science and Simulation in Transportation Research: IGI Global, pp. 1-29, 2014.
Rinzivillo, S., M. Gabbrielli, M. Nanni, L. Pappalardo, D. Pedreschi, and F. Giannotti, "The purpose of motion: Learning activities from Individual Mobility Networks", International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014, 2014.
de Lira, V M., S. Rinzivillo, V C. Times, and C. Renso, "{MAPMOLTY:} {A} Web Tool for Discovering Place Loyalty Based on Mobile Crowdsource Data", Web Engineering, 14th International Conference, {ICWE} 2014, Toulouse, France, July 1-4, 2014. Proceedings, pp. 528–531, 2014.
de Lira, V M., S. Rinzivillo, C. Renso, V C. Times, and P{\'ı}cia. C. A. R. Tedesco, "Investigating semantic regularity of human mobility lifestyle", 18th International Database Engineering {&} Applications Symposium, {IDEAS} 2014, Porto, Portugal, July 7-9, 2014, Porto, Portugal, ACM, pp. 314–317, 2014.
Furletti, B., R. Trasarti, M. Gabbrielli, M. Nanni, and D. Pedreschi, "Big data analytics for smart mobility: a case study", EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD), Athens, Greece, 03/2014.
Monreale, A., W. Hui Wang, F. Pratesi, S. Rinzivillo, D. Pedreschi, G. Andrienko, and N. Andrienko, "Privacy-Preserving Distributed Movement Data Aggregation", Geographic Information Science at the Heart of Europe: Springer International Publishing, pp. 225-245, 2013.


Many scientists point out that the goal of social sciences is not simply to understand how people behave in large groups, but to understand what motivates individuals to behave the way they do.

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Start Date
1 October 2011
End Date
1 September 2014
Istituto di Scienza e Tecnologie dell’Informazione, National Research Council of Italy (ISTI-CNR)