01300nas a2200217   4500008004100000020001800041245008900059210006900148260005900217520057700276100002100853700001900874700001600893700002500909700002900934700001700963700002100980700001901001700001801020856004401038       2022                        eng d  a978145039529800aConnected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)0 aConnected Vehicle Simulation Framework for Parking Occupancy Pre  aNew York, NY, USAbAssociation for Computing Machinery3 aThis paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.1 aResce, Pierpaolo1 aVorwerk, Lukas1 aHan, Zhiwei1 aCornacchia, Giuliano1 aAlamdari, Omid, Isfahani1 aNanni, Mirco1 aPappalardo, Luca1 aWeimer, Daniel1 aLiu, Yuanting  uhttps://doi.org/10.1145/3557915.356099501538nas a2200181   4500008004100000020001800041245005000059210005000109260005900159520097300218100002501191700001801216700002001234700001701254700002001271700002101291856004401312       2022                        eng d  a978145039529800aHow Routing Strategies Impact Urban Emissions0 aHow Routing Strategies Impact Urban Emissions  aNew York, NY, USAbAssociation for Computing Machinery3 aNavigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.1 aCornacchia, Giuliano1 aBöhm, Matteo1 aMauro, Giovanni1 aNanni, Mirco1 aPedreschi, Dino1 aPappalardo, Luca  uhttps://doi.org/10.1145/3557915.356097701965nas a2200145   4500008004100000022001400041245013400055210006900189300000800258490000700266520145600273100002501729700002101754856004401775       2021                        eng d  a2220-996400aA Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together0 aMechanistic DataDriven Approach to Synthesize Human Mobility Con  a5990 v103 aModelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.1 aCornacchia, Giuliano1 aPappalardo, Luca  uhttps://www.mdpi.com/2220-9964/10/9/59900501nas a2200109   4500008004100000245010900041210006900150260000700219100002500226700002100251856011900272       2021                        eng d00aSTS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together0 aSTSEPR Modelling individual mobility considering the spatial tem  c051 aCornacchia, Giuliano1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/sts-epr-modelling-individual-mobility-considering-spatial-temporal-and-social00485nas a2200109   4500008004100000245008100041210006900122100002500191700002100216700002100237856011700258       2020                        eng d00aModelling Human Mobility considering Spatial, Temporal and Social Dimensions0 aModelling Human Mobility considering Spatial Temporal and Social1 aCornacchia, Giuliano1 aRossetti, Giulio1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/modelling-human-mobility-considering-spatial-temporal-and-social-dimensions