@conference {1493,
	title = {Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)},
	booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
	year = {2022},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {This paper demonstrates a simulation framework that collects data about connected vehicles{\textquoteright} 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.},
	isbn = {9781450395298},
	doi = {10.1145/3557915.3560995},
	url = {https://doi.org/10.1145/3557915.3560995},
	author = {Resce, Pierpaolo and Vorwerk, Lukas and Han, Zhiwei and Cornacchia, Giuliano and Alamdari, Omid Isfahani and Mirco Nanni and Luca Pappalardo and Weimer, Daniel and Liu, Yuanting}
}
@conference {1492,
	title = {How Routing Strategies Impact Urban Emissions},
	booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
	year = {2022},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {Navigation apps use routing algorithms to suggest the best path to reach a user{\textquoteright}s desired destination. Although undoubtedly useful, navigation apps{\textquoteright} 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{\textquoteright}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{\textquoteright} 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.},
	isbn = {9781450395298},
	doi = {10.1145/3557915.3560977},
	url = {https://doi.org/10.1145/3557915.3560977},
	author = {Cornacchia, Giuliano and B{\"o}hm, Matteo and Giovanni Mauro and Mirco Nanni and Dino Pedreschi and Luca Pappalardo}
}
@article {1463,
	title = {A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together},
	journal = {ISPRS International Journal of Geo-Information},
	volume = {10},
	number = {9},
	year = {2021},
	pages = {599},
	abstract = {Modelling 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.},
	issn = {2220-9964},
	doi = {10.3390/ijgi10090599},
	url = {https://www.mdpi.com/2220-9964/10/9/599},
	author = {Cornacchia, Giuliano and Luca Pappalardo}
}
@article {1449,
	title = {STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together},
	year = {2021},
	month = {05},
	doi = {10.1016/j.procs.2021.03.035},
	author = {Cornacchia, Giuliano and Luca Pappalardo}
}
@article {1368,
	title = {Modelling Human Mobility considering Spatial, Temporal and Social Dimensions},
	journal = {arXiv preprint arXiv:2007.02371},
	year = {2020},
	author = {Cornacchia, Giuliano and Giulio Rossetti and Luca Pappalardo}
}
