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2023
Journal article
Open Access
A dataset to assess mobility changes in Chile following local quarantines
Pappalardo L., Cornacchia G., Navarro V., Bravo L., Ferres L.
Fighting the COVID-19 pandemic, most countries have implemented non-pharmaceutical interventions like wearing masks, physical distancing, lockdown, and travel restrictions. Because of their economic and logistical effects, tracking mobility changes during quarantines is crucial in assessing their efficacy and predicting the virus spread. Unlike many other heavily affected countries, Chile implemented quarantines at a more localized level, shutting down small administrative zones, rather than the whole country or large regions. Given the non-obvious effects of these localized quarantines, tracking mobility becomes even more critical in Chile. To assess the impact on human mobility of the localized quarantines, we analyze a mobile phone dataset made available by Telefónica Chile, which comprises 31 billion eXtended Detail Records and 5.4 million users covering the period February 26th to September 20th, 2020. From these records, we derive three epidemiologically relevant metrics describing the mobility within and between comunas. The datasets made available may be useful to understand the effect of localized quarantines in containing the COVID-19 pandemic.Source: Scientific data 10 (2023). doi:10.1038/s41597-022-01893-3
DOI: 10.1038/s41597-022-01893-3
Project(s): SoBigData-PlusPlus
Metrics:
Pappalardo L., Cornacchia G., Navarro V., Bravo L., Ferres L.
Fighting the COVID-19 pandemic, most countries have implemented non-pharmaceutical interventions like wearing masks, physical distancing, lockdown, and travel restrictions. Because of their economic and logistical effects, tracking mobility changes during quarantines is crucial in assessing their efficacy and predicting the virus spread. Unlike many other heavily affected countries, Chile implemented quarantines at a more localized level, shutting down small administrative zones, rather than the whole country or large regions. Given the non-obvious effects of these localized quarantines, tracking mobility becomes even more critical in Chile. To assess the impact on human mobility of the localized quarantines, we analyze a mobile phone dataset made available by Telefónica Chile, which comprises 31 billion eXtended Detail Records and 5.4 million users covering the period February 26th to September 20th, 2020. From these records, we derive three epidemiologically relevant metrics describing the mobility within and between comunas. The datasets made available may be useful to understand the effect of localized quarantines in containing the COVID-19 pandemic.Source: Scientific data 10 (2023). doi:10.1038/s41597-022-01893-3
DOI: 10.1038/s41597-022-01893-3
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository 
| www.nature.com | CNR ExploRA
2023
Contribution to book
Unknown
Towards a social Artificial Intelligence
Pedreschi D., Dignum F., Morini V., Pansanella V., Cornacchia G.
Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.Source: Human-Centered Artificial Intelligence, edited by Chetouani M., Dignum V., Lukowicz P., Sierra C., pp. 415–428, 2023
DOI: 10.1007/978-3-031-24349-3_21
Metrics:
Pedreschi D., Dignum F., Morini V., Pansanella V., Cornacchia G.
Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.Source: Human-Centered Artificial Intelligence, edited by Chetouani M., Dignum V., Lukowicz P., Sierra C., pp. 415–428, 2023
DOI: 10.1007/978-3-031-24349-3_21
Metrics:
See at: link.springer.com | CNR ExploRA
2023
Conference article
Open Access
Human mobility, AI assistants, and urban emissions: an insidious triangle
Pappalardo L., Bohm M., Cornacchia G., Mauro G., Pedreschi D., Nanni M.
Transportation remains a significant contributor to greenhouse gas emissions, with a substantial proportion originating from road transport and passenger travel in particular. Today, the relationship between transportation and urban emissions is even more complex, given the increasingly prevalent role and the pervasiveness of AI-based GPS navigation systems such as Google Maps and TomTom. While these services offer benefits to individual drivers, they can also exacerbate congestion and increase pollution if too many drivers are directed onto the same route. In this article, we provide two examples from our research group that explore the impact of vehicular transportation and mobility-AI-based applications on urban emissions. By conducting realistic simulations and studying the impact of GPS navigation systems on emissions, we provide insights into the potential for mitigating transportation emissions and developing policies that promote sustainable urban mobility. Our examples demonstrate how vehicle-generated emissions can be reduced and how studying the impact of GPS navigation systems on emissions can lead to unexpected findings. Overall, our analysis suggests that it is crucial to consider the impact of emerging technologies on transportation and emissions, and to develop strategies that promote sustainable mobility while ensuring the optimal use of these tools.Source: Ital-IA 2023: 3rd National Conference on Artificial Intelligence, pp. 585–589, Pisa, Italy, 29-31/05/2023
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Pappalardo L., Bohm M., Cornacchia G., Mauro G., Pedreschi D., Nanni M.
Transportation remains a significant contributor to greenhouse gas emissions, with a substantial proportion originating from road transport and passenger travel in particular. Today, the relationship between transportation and urban emissions is even more complex, given the increasingly prevalent role and the pervasiveness of AI-based GPS navigation systems such as Google Maps and TomTom. While these services offer benefits to individual drivers, they can also exacerbate congestion and increase pollution if too many drivers are directed onto the same route. In this article, we provide two examples from our research group that explore the impact of vehicular transportation and mobility-AI-based applications on urban emissions. By conducting realistic simulations and studying the impact of GPS navigation systems on emissions, we provide insights into the potential for mitigating transportation emissions and developing policies that promote sustainable urban mobility. Our examples demonstrate how vehicle-generated emissions can be reduced and how studying the impact of GPS navigation systems on emissions can lead to unexpected findings. Overall, our analysis suggests that it is crucial to consider the impact of emerging technologies on transportation and emissions, and to develop strategies that promote sustainable mobility while ensuring the optimal use of these tools.Source: Ital-IA 2023: 3rd National Conference on Artificial Intelligence, pp. 585–589, Pisa, Italy, 29-31/05/2023
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
One-shot traffic assignment with forward-looking penalization
Cornacchia G., Nanni M., Pappalardo L.
Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider realtime traffic conditions, resulting in inefficient route assignments. This paper introduces Metis, a coordinated, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of Metis against state-of-the-art oneshot methods. Compared to the best baseline, Metis significantly reduces CO2 emissions by 18% in Milan, 28% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes Metis as a promising solution for optimizing TA and urban transportation systems.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3625637
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Metrics:
Cornacchia G., Nanni M., Pappalardo L.
Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider realtime traffic conditions, resulting in inefficient route assignments. This paper introduces Metis, a coordinated, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of Metis against state-of-the-art oneshot methods. Compared to the best baseline, Metis significantly reduces CO2 emissions by 18% in Milan, 28% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes Metis as a promising solution for optimizing TA and urban transportation systems.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3625637
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
dl.acm.org | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
Connected vehicle simulation framework for parking occupancy prediction (demo paper)
Resce P., Vorwerk L., Han Z., Cornacchia G., Alamdari O. I., Nanni M., Pappalardo L., Weimer D., Liu Y.
This 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.Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022
DOI: 10.1145/3557915.3560995
Project(s): HumanE-AI-Net
Metrics:
Resce P., Vorwerk L., Han Z., Cornacchia G., Alamdari O. I., Nanni M., Pappalardo L., Weimer D., Liu Y.
This 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.Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022
DOI: 10.1145/3557915.3560995
Project(s): HumanE-AI-Net
Metrics:
See at:
ISTI Repository | dl.acm.org 
| doi.org | CNR ExploRA
2022
Conference article
Open Access
How routing strategies impact urban emissions
Cornacchia G., Bohm M., Mauro G., Nanni M., Pedreschi D., Pappalardo L.
Navigation 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.Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022
DOI: 10.1145/3557915.3560977
DOI: 10.48550/arxiv.2207.01456
Project(s): SoBigData-PlusPlus
Metrics:
Cornacchia G., Bohm M., Mauro G., Nanni M., Pedreschi D., Pappalardo L.
Navigation 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.Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022
DOI: 10.1145/3557915.3560977
DOI: 10.48550/arxiv.2207.01456
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | ISTI Repository | dl.acm.org | doi.org | doi.org | CNR ExploRA
2021
Journal article
Open Access
STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together
Cornacchia G., Pappalardo L.
Modelling human mobility is crucial in several scientific areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing models focus on the spatial and temporal dimensions of mobility only, while the social dimension is often neglected. On other hand, models that embed a social mechanism have trivial or unrealistic spatial and temporal mechanisms. We propose STS-EPR, a mechanistic model that captures the spatial, temporal, and social dimensions of human mobility together. Our results show that STS-EPR generates realistic trajectories, making it better than models that lack either in the social, the spatial, or the temporal mechanisms. STS-EPR is a step towards the design of mechanistic models that can capture all the aspects of human mobility in a comprehensive way.Source: Procedia computer science 184 (2021): 258–265. doi:10.1016/j.procs.2021.03.035
DOI: 10.1016/j.procs.2021.03.035
Project(s): SoBigData-PlusPlus
Metrics:
Cornacchia G., Pappalardo L.
Modelling human mobility is crucial in several scientific areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing models focus on the spatial and temporal dimensions of mobility only, while the social dimension is often neglected. On other hand, models that embed a social mechanism have trivial or unrealistic spatial and temporal mechanisms. We propose STS-EPR, a mechanistic model that captures the spatial, temporal, and social dimensions of human mobility together. Our results show that STS-EPR generates realistic trajectories, making it better than models that lack either in the social, the spatial, or the temporal mechanisms. STS-EPR is a step towards the design of mechanistic models that can capture all the aspects of human mobility in a comprehensive way.Source: Procedia computer science 184 (2021): 258–265. doi:10.1016/j.procs.2021.03.035
DOI: 10.1016/j.procs.2021.03.035
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | www.sciencedirect.com | CNR ExploRA
2021
Journal article
Open Access
A mechanistic data-driven approach to synthesize human mobility considering the spatial, temporal, and social dimensions together
Cornacchia G., Pappalardo L.
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.Source: ISPRS international journal of geo-information 10 (2021). doi:10.3390/ijgi10090599
DOI: 10.3390/ijgi10090599
Project(s): SoBigData-PlusPlus
Metrics:
Cornacchia G., Pappalardo L.
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.Source: ISPRS international journal of geo-information 10 (2021). doi:10.3390/ijgi10090599
DOI: 10.3390/ijgi10090599
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | www.mdpi.com | CNR ExploRA
2020
Master thesis
Unknown
Modeling Human Mobility considering Spatial, Temporal and Social Dimensions
Cornacchia G.
The analysis of human mobility is crucial in several areas, from urban planning to epidemic modeling, estimation of migratory flows and traffic forecasting.However, mobility data (e.g., Call Detail Records and GPS traces from vehicles or smartphones) are sensitive since it is possible to infer personal information even from anonymized datasets.A solution to dealing with this privacy issue is to use synthetic and realistic trajectories generated by proper generative models.Existing mechanistic generative models usually consider the spatial and temporal dimensions only. In this thesis, we select as a baseline model GeoSim, which considers the social dimension together with spatial and temporal dimensions during the generation of the synthetic trajectories.Our contribution in the field of the human mobility consists of including, incrementally, three mobility mechanisms, specifically the introduction of the distance and the use of a gravity-model in the location selection phase, finally, we include a diary generator, an algorithm capable to capture the tendency of humans to follow or break their routine, improving the modeling capability of the GeoSim model.
We show that the three implemented models, obtained from GeoSim with the introduction of the mobility mechanisms, can reproduce the statistical proprieties of real trajectories, in all the three dimensions, more accurately than GeoSim.Project(s): SoBigData
Cornacchia G.
The analysis of human mobility is crucial in several areas, from urban planning to epidemic modeling, estimation of migratory flows and traffic forecasting.However, mobility data (e.g., Call Detail Records and GPS traces from vehicles or smartphones) are sensitive since it is possible to infer personal information even from anonymized datasets.A solution to dealing with this privacy issue is to use synthetic and realistic trajectories generated by proper generative models.Existing mechanistic generative models usually consider the spatial and temporal dimensions only. In this thesis, we select as a baseline model GeoSim, which considers the social dimension together with spatial and temporal dimensions during the generation of the synthetic trajectories.Our contribution in the field of the human mobility consists of including, incrementally, three mobility mechanisms, specifically the introduction of the distance and the use of a gravity-model in the location selection phase, finally, we include a diary generator, an algorithm capable to capture the tendency of humans to follow or break their routine, improving the modeling capability of the GeoSim model.
We show that the three implemented models, obtained from GeoSim with the introduction of the mobility mechanisms, can reproduce the statistical proprieties of real trajectories, in all the three dimensions, more accurately than GeoSim.Project(s): SoBigData
See at: etd.adm.unipi.it | CNR ExploRA