2025
Journal article
Open Access
Dynamic models of gentrification
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
DOI: 10.1142/s0219525925400065
DOI: 10.48550/arxiv.2410.18004
Project(s): Community Detection And Dynamics in Temporal Networks
, SoBigData-PlusPlus
Metrics:
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
DOI: 10.1142/s0219525925400065
DOI: 10.48550/arxiv.2410.18004
Project(s): Community Detection And Dynamics in Temporal Networks
, SoBigData-PlusPlus Metrics:
See at:
arXiv.org e-Print Archive 
| Archivio istituzionale della Ricerca - Scuola Normale Superiore | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR IRIS | Oxford University Research Archive | www.worldscientific.com | Advances in Complex Systems 
| doi.org | CNR IRIS
2025
Conference article
Restricted
What tugs at your heartstrings? Exploring flow, and affect recognition through HRV, while video gaming
Sajno E., Rossi A., Beretta A., Novielli N., Pappalardo L., Riva G.
This study explores the detection of flow states through affective computing, focusing on physiological measures such as Heart Rate Variability (HRV) during Tetris gameplay. Flow, characterized by a balance between challenge and skill, is a state of complete absorption that is complex to detect. We experimented with different difficulty levels of Tetris to elicit flow states and recorded interbeat intervals using a Polar H10 chest band. We refined the experimental protocol through a pilot study: 5 Tetris levels were chosen so that participants reported a suitable variance in difficulty. During the study, 53 participants' HRV metrics were analyzed alongside self-reported flow and affective states using questionnaires. The data analysis involved classical statistical methods and machine learning algorithms to classify flow and affective states based on HRV features. SVC and RandomForest algorithms achieved high accuracy in predicting flow-related categories (48% for balance, 52% for skill, 49% for challenge), while affective states results were more various (44% for dominance, 44% for valence, 43% for arousal, using respectively BernoulliNB, GaussianNB, and ExtraTrees). The results indicate distinct HRV patterns associated with flow and affective states, suggesting that HRV is a viable indicator for flow detection. Further research is needed to validate these findings with more comprehensive experimental designs and diverse realworld applications.DOI: 10.1109/aciiw63320.2024.00045
Metrics:
Sajno E., Rossi A., Beretta A., Novielli N., Pappalardo L., Riva G.
This study explores the detection of flow states through affective computing, focusing on physiological measures such as Heart Rate Variability (HRV) during Tetris gameplay. Flow, characterized by a balance between challenge and skill, is a state of complete absorption that is complex to detect. We experimented with different difficulty levels of Tetris to elicit flow states and recorded interbeat intervals using a Polar H10 chest band. We refined the experimental protocol through a pilot study: 5 Tetris levels were chosen so that participants reported a suitable variance in difficulty. During the study, 53 participants' HRV metrics were analyzed alongside self-reported flow and affective states using questionnaires. The data analysis involved classical statistical methods and machine learning algorithms to classify flow and affective states based on HRV features. SVC and RandomForest algorithms achieved high accuracy in predicting flow-related categories (48% for balance, 52% for skill, 49% for challenge), while affective states results were more various (44% for dominance, 44% for valence, 43% for arousal, using respectively BernoulliNB, GaussianNB, and ExtraTrees). The results indicate distinct HRV patterns associated with flow and affective states, suggesting that HRV is a viable indicator for flow detection. Further research is needed to validate these findings with more comprehensive experimental designs and diverse realworld applications.DOI: 10.1109/aciiw63320.2024.00045
Metrics:
See at:
doi.org | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Journal article
Open Access
Dynamic models of gentrification
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
DOI: 10.1142/s0219525925400065
DOI: 10.48550/arxiv.2410.18004
Project(s): Community Detection And Dynamics in Temporal Networks, SoBigData-PlusPlus , Urban Artificial Intelligence
Metrics:
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
DOI: 10.1142/s0219525925400065
DOI: 10.48550/arxiv.2410.18004
Project(s): Community Detection And Dynamics in Temporal Networks
, SoBigData-PlusPlus , Urban Artificial IntelligenceMetrics:
See at:
arXiv.org e-Print Archive | Archivio istituzionale della Ricerca - Scuola Normale Superiore | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR IRIS | Oxford University Research Archive | www.worldscientific.com | Advances in Complex Systems | doi.org | CNR IRIS
2025
Book
Open Access
Preface to INTSYS 2024
Kocian A., Milazzo P., Martins A. L., Nanni M., Pappalardo L.
An abstract is not available
Kocian A., Milazzo P., Martins A. L., Nanni M., Pappalardo L.
An abstract is not available
See at:
CNR IRIS | link.springer.com | CNR IRIS
2025
Contribution to book
Open Access
Preface to 7th International Workshop on Big Mobility Data Analytics (BMDA)
Nanni M., Pelekis N., Tampakis P., Zeitouni K., Sakr M., Renso C., Soares A., Artikis A., Theodoridis Y., Damiani M. L., Zissis D., Raffaetà A., Doulkeridis C., Kim K. -S., Ferhatosmanoglu H., Patroumpas K., Zeinalipour D., Coelho Da Silva T. L., Tserpes K., Andersen N. S., Pfoser D., Pappalardo L., Guidotti R., Kontopoulos I., Lu H., Nørvåg K., Andrienko N.
An abstract is not available
Nanni M., Pelekis N., Tampakis P., Zeitouni K., Sakr M., Renso C., Soares A., Artikis A., Theodoridis Y., Damiani M. L., Zissis D., Raffaetà A., Doulkeridis C., Kim K. -S., Ferhatosmanoglu H., Patroumpas K., Zeinalipour D., Coelho Da Silva T. L., Tserpes K., Andersen N. S., Pfoser D., Pappalardo L., Guidotti R., Kontopoulos I., Lu H., Nørvåg K., Andrienko N.
An abstract is not available
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Conference article
Restricted
Explaining urban vehicle emissions in Rome
Bohm M., Reyes P., Nanni M., Pappalardo L.
Urban emissions are a significant challenge for city livability. Our work focuses on studying vehicle emissions in cities, using spatial and non-spatial models to understand their relationships with various urban features. We find that the spatial model demonstrates better performance and provides powerful insights into the influence of different predictors in various city areas. Our findings reveal that CO2 emissions in Rome are primarily linked to the presence of main arterial roads, population density, and road network density. However, the importance of these factors varies across different areas of the city. We also performed a what-if analysis to show that limiting the circulation of highly polluting vehicles may help reduce emissions, especially in city centres. Our research contributes to a better understanding of the complex relationships between the urban environment and the spatial variability of vehicle emissions in Rome.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15244, pp. 303-315. Pisa, Italy, 14–16/10/2024
DOI: 10.1007/978-3-031-78980-9_19
Project(s): SoBigData-PlusPlus
Metrics:
Bohm M., Reyes P., Nanni M., Pappalardo L.
Urban emissions are a significant challenge for city livability. Our work focuses on studying vehicle emissions in cities, using spatial and non-spatial models to understand their relationships with various urban features. We find that the spatial model demonstrates better performance and provides powerful insights into the influence of different predictors in various city areas. Our findings reveal that CO2 emissions in Rome are primarily linked to the presence of main arterial roads, population density, and road network density. However, the importance of these factors varies across different areas of the city. We also performed a what-if analysis to show that limiting the circulation of highly polluting vehicles may help reduce emissions, especially in city centres. Our research contributes to a better understanding of the complex relationships between the urban environment and the spatial variability of vehicle emissions in Rome.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15244, pp. 303-315. Pisa, Italy, 14–16/10/2024
DOI: 10.1007/978-3-031-78980-9_19
Project(s): SoBigData-PlusPlus
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | link.springer.com
2025
Journal article
Open Access
Mixing individual and collective behaviors to predict out-of-routine mobility
Bontorin S., Centellegher S., Gallottimr., Pappalardo L., Lepri B., Luca M.
Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviors. Our study introduces an approach that dynamically integrates individual and collective mobility behaviors, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across five US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. The spatial analysis highlights the model’s effectiveness near urban areas with a high density of points of interest, where collective behaviors strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviors, our approach offers transparent and accurate predictions, which are crucial for addressing contemporary mobility challenges.Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, vol. 122 (issue 17)
DOI: 10.1073/pnas.2414848122
DOI: 10.48550/arxiv.2404.02740
Project(s): SoBigData-PlusPlus
Metrics:
Bontorin S., Centellegher S., Gallottimr., Pappalardo L., Lepri B., Luca M.
Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviors. Our study introduces an approach that dynamically integrates individual and collective mobility behaviors, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across five US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. The spatial analysis highlights the model’s effectiveness near urban areas with a high density of points of interest, where collective behaviors strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviors, our approach offers transparent and accurate predictions, which are crucial for addressing contemporary mobility challenges.Source: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, vol. 122 (issue 17)
DOI: 10.1073/pnas.2414848122
DOI: 10.48550/arxiv.2404.02740
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | Proceedings of the National Academy of Sciences | CNR IRIS | www.pnas.org | doi.org | CNR IRIS | Proceedings of the National Academy of Sciences
2024
Journal article
Open Access
Navigation services and urban sustainability
Cornacchia G, Nanni M, Pedreschi D, Pappalardo L
The The rise of socio-technical systems in which humans interact with various forms of Artificial Intelligence, including assistants and recommenders, multiplies the possibility for the emergence of large-scale social behavior, possibly with unintended negative consequences. In this work, we discuss a particularly interesting case, i.e., navigation services' impact on urban emissions, showing through simulations that the sum of many individually "optimal" choices may have unintended negative outcomes because such choices influence and interfere with each other on top of shared resources. To prove this point, we demonstrate how the introduction of a random component in the path suggestion phase may help to relieve the effect of collective and individual choices on the urban environment in terms of urban emissions.Source: FLUCTUATION AND NOISE LETTERS, vol. 23 (issue 3)
DOI: 10.1142/s0219477524500160
Project(s): HumanE-AI-Net, XAI , SoBigData-PlusPlus
Metrics:
Cornacchia G, Nanni M, Pedreschi D, Pappalardo L
The The rise of socio-technical systems in which humans interact with various forms of Artificial Intelligence, including assistants and recommenders, multiplies the possibility for the emergence of large-scale social behavior, possibly with unintended negative consequences. In this work, we discuss a particularly interesting case, i.e., navigation services' impact on urban emissions, showing through simulations that the sum of many individually "optimal" choices may have unintended negative outcomes because such choices influence and interfere with each other on top of shared resources. To prove this point, we demonstrate how the introduction of a random component in the path suggestion phase may help to relieve the effect of collective and individual choices on the urban environment in terms of urban emissions.Source: FLUCTUATION AND NOISE LETTERS, vol. 23 (issue 3)
DOI: 10.1142/s0219477524500160
Project(s): HumanE-AI-Net
, XAI , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | Fluctuation and Noise Letters | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
Measuring the impact of road removal on vehicular CO2 emissions
Baccile S., Cornacchia G., Pappalardo L.
Transportation networks face escalating challenges to cater to increased mobility demand while addressing traffic congestion. Traditional remedies, such as adding roads, can paradoxically worsen congestion, as seen in Braess’s paradox. This study emphasizes the potential benefits of strategically closing roads to alleviate congestion and carbon emissions. Milan serves as a case study, where various road closure strategies were tested to identify scenarios where strategic removal not only eased congestion but also significantly reduced CO2 emissions. The findings provide practical insights for urban planners and policymakers, offering a roadmap to develop more efficient and eco-friendly urban transportation systems.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3651. Paestum, Italy, 25/03/2025
Project(s): SoBigData-PlusPlus
Baccile S., Cornacchia G., Pappalardo L.
Transportation networks face escalating challenges to cater to increased mobility demand while addressing traffic congestion. Traditional remedies, such as adding roads, can paradoxically worsen congestion, as seen in Braess’s paradox. This study emphasizes the potential benefits of strategically closing roads to alleviate congestion and carbon emissions. Milan serves as a case study, where various road closure strategies were tested to identify scenarios where strategic removal not only eased congestion but also significantly reduced CO2 emissions. The findings provide practical insights for urban planners and policymakers, offering a roadmap to develop more efficient and eco-friendly urban transportation systems.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3651. Paestum, Italy, 25/03/2025
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Contribution to book
Open Access
Message from the Demo Chairs
Pappalardo L., Tampakis P., Jayaraman P. P.
An abstract is not availableDOI: 10.1109/mdm61037.2024.00008
Metrics:
Pappalardo L., Tampakis P., Jayaraman P. P.
An abstract is not availableDOI: 10.1109/mdm61037.2024.00008
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | University of Southern Denmark Research Output
2024
Conference article
Open Access
From streets to screens: a deep dive into urban configuration and Google reviews
Hacar M., Öztürk Hacar O., Altafini D., Pappalardo L., Gülgen F., Cutini V.
The experiences of urban visitors are frequently captured and shared on various websites and social platforms through reviews and ratings. These "digital footprints" help us understand what visitors think about a certain place. High visitor traffic may stem from the urban layout and configuration, such as the importance of a street, the building characteristic, or the location of the place within an urban settlement. Hence, these digital footprints are shaped by a complex interplay of spatial, emotional, self-organizational, and socio-behavioural factors. This presents us with compelling research questions: To what extent does urban configuration influence these digital footprints? And how exactly does this dynamic play out? This research examines the intricate relationship between urban spatial characteristics and digital footprints in the realm of urban tourism. Our approach taps into Space Syntax metrics and associates them with places, extracted as points of interest from Google Maps API. This framework helps ascertain what is the linkage between urban configuration and visitors’ digital activities, to reveal another layer of social behaviour within the cities. Our research centred on the historic city of Sassi di Matera, Italy, distinguished by its cave dwellings and intricate pathways. Initial findings from our research present significant correlations between Space Syntax metrics and visitors’ reviewing activities at specific places. By correlating the configurational patterns with reviews and ratings, we observed that these places are influenced by surrounding space, and that this situation varies depending on the type of place.
Hacar M., Öztürk Hacar O., Altafini D., Pappalardo L., Gülgen F., Cutini V.
The experiences of urban visitors are frequently captured and shared on various websites and social platforms through reviews and ratings. These "digital footprints" help us understand what visitors think about a certain place. High visitor traffic may stem from the urban layout and configuration, such as the importance of a street, the building characteristic, or the location of the place within an urban settlement. Hence, these digital footprints are shaped by a complex interplay of spatial, emotional, self-organizational, and socio-behavioural factors. This presents us with compelling research questions: To what extent does urban configuration influence these digital footprints? And how exactly does this dynamic play out? This research examines the intricate relationship between urban spatial characteristics and digital footprints in the realm of urban tourism. Our approach taps into Space Syntax metrics and associates them with places, extracted as points of interest from Google Maps API. This framework helps ascertain what is the linkage between urban configuration and visitors’ digital activities, to reveal another layer of social behaviour within the cities. Our research centred on the historic city of Sassi di Matera, Italy, distinguished by its cave dwellings and intricate pathways. Initial findings from our research present significant correlations between Space Syntax metrics and visitors’ reviewing activities at specific places. By correlating the configurational patterns with reviews and ratings, we observed that these places are influenced by surrounding space, and that this situation varies depending on the type of place.
See at:
cyprusconferences.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
The role of relocation policies in urban segregation dynamics
Mauro G., Pappalardo L.
This study addresses a gap in the existing literature on the Schelling segregation model by conducting a comprehensive qualitative assessment of various relocation policies. We introduce novel Schelling models driven by different relocation policies and analyse their impact on the convergence time and final segregation levels. Our findings demonstrate that all policies result in segregation levels within bounds established by policies where agents relocate to maximize their happiness. Notably, a policy ensuring the minimum improvement in agent segregation significantly reduces the model’s convergence time. These results underscore the potential influence of relocation policies, such as those employed by online recommenders in real estate platforms, on societal segregation dynamics. The study provides valuable insights into potential strategies for mitigating and decelerating segregation through tailored recommendations.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3651. Paestum, Italy, 25/03/2024
Project(s): Urban Artificial Intelligence
Mauro G., Pappalardo L.
This study addresses a gap in the existing literature on the Schelling segregation model by conducting a comprehensive qualitative assessment of various relocation policies. We introduce novel Schelling models driven by different relocation policies and analyse their impact on the convergence time and final segregation levels. Our findings demonstrate that all policies result in segregation levels within bounds established by policies where agents relocate to maximize their happiness. Notably, a policy ensuring the minimum improvement in agent segregation significantly reduces the model’s convergence time. These results underscore the potential influence of relocation policies, such as those employed by online recommenders in real estate platforms, on societal segregation dynamics. The study provides valuable insights into potential strategies for mitigating and decelerating segregation through tailored recommendations.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3651. Paestum, Italy, 25/03/2024
Project(s): Urban Artificial Intelligence
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Contribution to book
Open Access
Message from the MAURO 2024 Workshop Chairs
Chondrodima E., Cornacchia G., Mauro G., Nanni M., Pappalardo L., Pugliese C.
An abstract is not availableDOI: 10.1109/mdm61037.2024.00011
Metrics:
Chondrodima E., Cornacchia G., Mauro G., Nanni M., Pappalardo L., Pugliese C.
An abstract is not availableDOI: 10.1109/mdm61037.2024.00011
Metrics:
See at:
CNR IRIS | www.computer.org | doi.org | CNR IRIS
2024
Journal article
Open Access
Human-AI coevolution
Pedreschi D., Pappalardo L., Ferragina E., Baeza-Yates R., Barabási A-L., Dignum F., Dignum V., Eliassi-Rad T., Giannotti F., Kertész J., Knott A., Ioannidis Y., Lukowicz P., Passarella A., Pentland A. S., Shawe-Taylor J., Vespignani A.
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.Source: ARTIFICIAL INTELLIGENCE, vol. 339
DOI: 10.1016/j.artint.2024.104244
DOI: 10.48550/arxiv.2306.13723
Project(s): HumanE-AI-Net, SAI: Social Explainable Artificial Intelligence , XAI , SoBigData-PlusPlus , Social Explainable Artificial Intelligence (SAI)
Metrics:
Pedreschi D., Pappalardo L., Ferragina E., Baeza-Yates R., Barabási A-L., Dignum F., Dignum V., Eliassi-Rad T., Giannotti F., Kertész J., Knott A., Ioannidis Y., Lukowicz P., Passarella A., Pentland A. S., Shawe-Taylor J., Vespignani A.
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.Source: ARTIFICIAL INTELLIGENCE, vol. 339
DOI: 10.1016/j.artint.2024.104244
DOI: 10.48550/arxiv.2306.13723
Project(s): HumanE-AI-Net
, SAI: Social Explainable Artificial Intelligence , XAI , SoBigData-PlusPlus , Social Explainable Artificial Intelligence (SAI) Metrics:
See at:
arXiv.org e-Print Archive | Artificial Intelligence | CNR IRIS | Publikationer från Umeå universitet | www.sciencedirect.com | doi.org | CNR IRIS | Hal
2024
Other
Open Access
Quantifying and mitigating the impact of vehicular routing on the urban environment
Cornacchia G., Pappalardo L., Nanni Mirco
Urbanization pressures cities to efficiently accommodate the increasing demand for mobility, making traffic optimization challenging due to the complex interplay be- tween road networks and traffic dynamics, as drivers’ routing choices significantly in- fluence one another. City-related services, such as navigation services (e.g., TomTom) and mobility policies (e.g., road closures), impact traffic patterns and emissions. Nav- igation services can unintentionally increase emissions when many vehicles converge on the same routes, while mobility policies may have counterintuitive effects on traffic. We propose a simulation framework to assess the impact of road closure policies and navigation services on the urban environment. We use this framework and find that targeted road closures in Milan can reduce emissions by up to 10%, while others can increase emissions by nearly 50%. Then, we examine navigation services’ impact on vehicular traffic and CO2 emissions, finding that they reduce emissions at low traffic loads. However, at high traffic loads and penetration rates, they cause conformist behavior, leading to inefficiencies and potentially higher emissions. To mitigate the conformist behavior induced by navigation services and reduce CO2 emissions, we propose three solutions: (i) an individualistic approach using existing Alternative Routing (AR) algorithms, (ii) Metis, a coordinated solution that coordinates drivers and dynamically estimates traffic to diversify routes, and (iii) Polaris, an individual AR algorithm which considers road popularity to optimize traffic distribution. Moti- vated by the varying effectiveness of AR solutions across cities, we study cities’ route diversification, defining shortest path instability and introducing diverCity, a metric to assess a city’s propensity towards route diversity. Analysis shows that diverCity benefits from extensive road networks, leading to less congestion. We also address the impact of mobility attractors on diverCity and propose mitigation strategies. This thesis comprehensively studies vehicular traffic dynamics, offering a simulation framework to evaluate the environmental impact of mobility policies and navigation services. In addition, it presents solutions to mitigate negative impacts and proposes metrics to quantify a city’s potential to offer route diversity.
Cornacchia G., Pappalardo L., Nanni Mirco
Urbanization pressures cities to efficiently accommodate the increasing demand for mobility, making traffic optimization challenging due to the complex interplay be- tween road networks and traffic dynamics, as drivers’ routing choices significantly in- fluence one another. City-related services, such as navigation services (e.g., TomTom) and mobility policies (e.g., road closures), impact traffic patterns and emissions. Nav- igation services can unintentionally increase emissions when many vehicles converge on the same routes, while mobility policies may have counterintuitive effects on traffic. We propose a simulation framework to assess the impact of road closure policies and navigation services on the urban environment. We use this framework and find that targeted road closures in Milan can reduce emissions by up to 10%, while others can increase emissions by nearly 50%. Then, we examine navigation services’ impact on vehicular traffic and CO2 emissions, finding that they reduce emissions at low traffic loads. However, at high traffic loads and penetration rates, they cause conformist behavior, leading to inefficiencies and potentially higher emissions. To mitigate the conformist behavior induced by navigation services and reduce CO2 emissions, we propose three solutions: (i) an individualistic approach using existing Alternative Routing (AR) algorithms, (ii) Metis, a coordinated solution that coordinates drivers and dynamically estimates traffic to diversify routes, and (iii) Polaris, an individual AR algorithm which considers road popularity to optimize traffic distribution. Moti- vated by the varying effectiveness of AR solutions across cities, we study cities’ route diversification, defining shortest path instability and introducing diverCity, a metric to assess a city’s propensity towards route diversity. Analysis shows that diverCity benefits from extensive road networks, leading to less congestion. We also address the impact of mobility attractors on diverCity and propose mitigation strategies. This thesis comprehensively studies vehicular traffic dynamics, offering a simulation framework to evaluate the environmental impact of mobility policies and navigation services. In addition, it presents solutions to mitigate negative impacts and proposes metrics to quantify a city’s potential to offer route diversity.
2024
Conference article
Open Access
Alternative routing based on road popularity
Cornacchia G., Lemma K., Pappalardo L.
Alternative routing in urban transportation is essential for minimizing environmental impact and improving road network efficiency. However, existing methods often neglect road popularity, increasing congestion and emissions. This study introduces Polaris, a novel alternative routing algorithm considering road popularity to optimize traffic distribution. Utilizing the concept of Kroad layers, Polaris effectively balances traffic loads across less popular roads, reducing the likelihood of congestion. Experiments conducted across three Italian cities demonstrate that Polaris significantly reduces the overuse of highly popular road edges, minimizes traversed regulated intersections, and lowers CO2 emissions compared to state-of-the-art alternative routing algorithms. This makes Polaris a promising solution for sustainable urban traffic management.DOI: 10.1145/3681779.3696836
Project(s): SoBigData-PlusPlus
Metrics:
Cornacchia G., Lemma K., Pappalardo L.
Alternative routing in urban transportation is essential for minimizing environmental impact and improving road network efficiency. However, existing methods often neglect road popularity, increasing congestion and emissions. This study introduces Polaris, a novel alternative routing algorithm considering road popularity to optimize traffic distribution. Utilizing the concept of Kroad layers, Polaris effectively balances traffic loads across less popular roads, reducing the likelihood of congestion. Experiments conducted across three Italian cities demonstrate that Polaris significantly reduces the overuse of highly popular road edges, minimizes traversed regulated intersections, and lowers CO2 emissions compared to state-of-the-art alternative routing algorithms. This makes Polaris a promising solution for sustainable urban traffic management.DOI: 10.1145/3681779.3696836
Project(s): SoBigData-PlusPlus
Metrics:
See at:
dl.acm.org | CNR IRIS | doi.org | CNR IRIS
2023
Journal article
Open Access
A survey on deep learning for human mobility
Luca M, Barlacchi G, Lepri B, Pappalardo L
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.Source: ACM COMPUTING SURVEYS, vol. 55 (issue 1)
DOI: 10.1145/3485125
DOI: 10.48550/arxiv.2012.02825
Project(s): SoBigData-PlusPlus
Metrics:
Luca M, Barlacchi G, Lepri B, Pappalardo L
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.Source: ACM COMPUTING SURVEYS, vol. 55 (issue 1)
DOI: 10.1145/3485125
DOI: 10.48550/arxiv.2012.02825
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | ACM Computing Surveys | dl.acm.org | CNR IRIS | ISTI Repository | ACM Computing Surveys | doi.org | CNR IRIS | CNR IRIS
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, vol. 10 (issue 1)
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, vol. 10 (issue 1)
DOI: 10.1038/s41597-022-01893-3
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | ISTI Repository | www.nature.com | CNR IRIS
2023
Journal article
Open Access
Trajectory test-train overlap in next-location prediction datasets
Luca M, Pappalardo L, Lepri B, Barlacchi G
Next-location prediction, consisting of forecasting a user's location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading. Several predictors have been proposed in the last few years to address it, including last-generation ones based on deep learning. This paper tests the generalization capability of these predictors on public mobility datasets, stratifying the datasets by whether the trajectories in the test set also appear fully or partially in the training set. We consistently discover a severe problem of trajectory overlapping in all analyzed datasets, highlighting that predictors memorize trajectories while having limited generalization capacities. We thus propose a methodology to rerank the outputs of the next-location predictors based on spatial mobility patterns. With these techniques, we significantly improve the predictors' generalization capability, with a relative improvement in the accuracy up to 96.15% on the trajectories that cannot be memorized (i.e., low overlap with the training set).Source: MACHINE LEARNING
DOI: 10.1007/s10994-023-06386-x
Project(s): SoBigData-PlusPlus
Metrics:
Luca M, Pappalardo L, Lepri B, Barlacchi G
Next-location prediction, consisting of forecasting a user's location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading. Several predictors have been proposed in the last few years to address it, including last-generation ones based on deep learning. This paper tests the generalization capability of these predictors on public mobility datasets, stratifying the datasets by whether the trajectories in the test set also appear fully or partially in the training set. We consistently discover a severe problem of trajectory overlapping in all analyzed datasets, highlighting that predictors memorize trajectories while having limited generalization capacities. We thus propose a methodology to rerank the outputs of the next-location predictors based on spatial mobility patterns. With these techniques, we significantly improve the predictors' generalization capability, with a relative improvement in the accuracy up to 96.15% on the trajectories that cannot be memorized (i.e., low overlap with the training set).Source: MACHINE LEARNING
DOI: 10.1007/s10994-023-06386-x
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2023
Journal article
Restricted
Future directions in human mobility science
Pappalardo L, Manley E, Sekara V, Alessandretti L
We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.Source: NATURE COMPUTATIONAL SCIENCE, vol. 3 (issue 7), pp. 588-600
DOI: 10.1038/s43588-023-00469-4
Project(s): SoBigData-PlusPlus
Metrics:
Pappalardo L, Manley E, Sekara V, Alessandretti L
We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.Source: NATURE COMPUTATIONAL SCIENCE, vol. 3 (issue 7), pp. 588-600
DOI: 10.1038/s43588-023-00469-4
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | CNR IRIS | www.nature.com