CNR Author
operator:
and / or
Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2023
Journal article
Open Access
Mobility constraints in segregation models
Gambetta D., Mauro G., Pappalardo L.
Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.Source: Scientific reports (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-023-38519-6
DOI: 10.1038/s41598-023-38519-6
DOI: 10.48550/arxiv.2305.10170
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus
Metrics:
Gambetta D., Mauro G., Pappalardo L.
Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.Source: Scientific reports (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-023-38519-6
DOI: 10.1038/s41598-023-38519-6
DOI: 10.48550/arxiv.2305.10170
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
Scientific Reports 
| ISTI Repository | www.nature.com | doi.org 
| 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
2022
Journal article
Open Access
Generating mobility networks with generative adversarial networks
Mauro G., Luca M., Longa A., Lepri B., Pappalardo L.
The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city's entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people's movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.Source: EPJ 11 (2022). doi:10.1140/epjds/s13688-022-00372-4
DOI: 10.1140/epjds/s13688-022-00372-4
Project(s): SoBigData-PlusPlus
Metrics:
Mauro G., Luca M., Longa A., Lepri B., Pappalardo L.
The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city's entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people's movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.Source: EPJ 11 (2022). doi:10.1140/epjds/s13688-022-00372-4
DOI: 10.1140/epjds/s13688-022-00372-4
Project(s): SoBigData-PlusPlus
Metrics:
See at:
EPJ Data Science | epjdatascience.springeropen.com | ISTI Repository | 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