01227nas a2200133   4500008004100000245007400041210006900115520077700184100002100961700002200982700002501004700002001029856004401049       2019                        eng d00aHuman Mobility from theory to practice: Data, Models and Applications0 aHuman Mobility from theory to practice Data Models and Applicati3 aThe inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library ”scikit-mobility” developed by the presenters of the tutorial.1 aPappalardo, Luca1 aBarlacchi, Gianni1 aPellungrini, Roberto1 aSimini, Filippo  uhttps://doi.org/10.1145/3308560.332009901949nas a2200133   4500008004100000022001400041245007300055210006900128260000800197520152300205100002101728700002001749856004601769       2017                        eng d  a1573-756X00aData-driven generation of spatio-temporal routines in human mobility0 aDatadriven generation of spatiotemporal routines in human mobili  cDec3 aThe generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.1 aPappalardo, Luca1 aSimini, Filippo  uhttps://doi.org/10.1007/s10618-017-0548-401571nas a2200181   4500008004100000245005600041210005600097260000700153490000600160520105400166100002101220700002001241700001801261700002001279700002101299700002801320856004101348       2015                        eng d00aReturners and explorers dichotomy in human mobility0 aReturners and explorers dichotomy in human mobility  c090 v63 aThe availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.1 aPappalardo, Luca1 aSimini, Filippo1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca1 aBarabasi, Albert-Laszlo  uhttp://dx.doi.org/10.1038/ncomms916600483nas a2200145   4500008004100000245005100041210005100092260000900143100002100152700002000173700001800193700002000211700002100231856008500252       2013                        eng d00aComparing General Mobility and Mobility by Car0 aComparing General Mobility and Mobility by Car  cSept1 aPappalardo, Luca1 aSimini, Filippo1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/comparing-general-mobility-and-mobility-car