CNR Author
operator:
and / or
Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
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
Conference article
Open Access
Barcelona effect: studying the instability of shortest paths in urban settings
Cornacchia G., Nanni M., Grassi F.
Human mobility is one of the important factors affecting the efficiency of cities and the quality of life of their dwellers. However, while city planners aim to improve the urban road network design to satisfy the local mobility demand and distribute traffic in an optimal way, the structure of cities across different areas and countries vary considerably and in complex ways, sometimes being the result of historical stratifications. One question that emerges, then, is how we can characterize cities in terms of (potential) traffic efficiency. In this work we aim to study the problem from a new perspective, introducing the concept of (shortest) path instability, which quantifies the tendency of a road network to provide very different travel alternatives for just slightly different trips. A notable case of that, which stimulated this research, is the city of Barcelona, where, apparently, reaching very close destinations might require very different routes. The concept is implemented and applied to two case studies at different spatial scales, one comparing the European capitals and the other comparing municipalities of an Italian region. Results show that path instability is heterogeneously distributed, with some largely unstable cities and others very stable, and it is not directly determined by simple city characteristics, such as the city size or its "smartness".Source: CEUR WORKSHOP PROCEEDINGS, vol. 3651. Paestum, Italy, 25/03/2025
Project(s): Green.Dat.AI
Cornacchia G., Nanni M., Grassi F.
Human mobility is one of the important factors affecting the efficiency of cities and the quality of life of their dwellers. However, while city planners aim to improve the urban road network design to satisfy the local mobility demand and distribute traffic in an optimal way, the structure of cities across different areas and countries vary considerably and in complex ways, sometimes being the result of historical stratifications. One question that emerges, then, is how we can characterize cities in terms of (potential) traffic efficiency. In this work we aim to study the problem from a new perspective, introducing the concept of (shortest) path instability, which quantifies the tendency of a road network to provide very different travel alternatives for just slightly different trips. A notable case of that, which stimulated this research, is the city of Barcelona, where, apparently, reaching very close destinations might require very different routes. The concept is implemented and applied to two case studies at different spatial scales, one comparing the European capitals and the other comparing municipalities of an Italian region. Results show that path instability is heterogeneously distributed, with some largely unstable cities and others very stable, and it is not directly determined by simple city characteristics, such as the city size or its "smartness".Source: CEUR WORKSHOP PROCEEDINGS, vol. 3651. Paestum, Italy, 25/03/2025
Project(s): Green.Dat.AI
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
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 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
Contribution to book
Metadata Only Access
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.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.DOI: 10.1007/978-3-031-24349-3_21
Metrics:
See at:
CNR IRIS | link.springer.com
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 systemsand consists in efficiently assigning routes to a collection oftrips. Existing TA algorithms often do not adequately consider realtimetraffic conditions, resulting in inefficient route assignments.This paper introduces Metis, a coordinated, one-shot TA algorithmthat combines alternative routing with edge penalization and informedroute scoring. We conduct experiments in several cities toevaluate the performance of Metis against state-of-the-art oneshotmethods. Compared to the best baseline, Metis significantlyreduces CO2 emissions by 18% in Milan, 28% in Florence, and 46%in Rome, improving trip distribution considerably while still havinglow computational time. Our study proposes Metis as a promisingsolution for optimizing TA and urban transportation systems.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 systemsand consists in efficiently assigning routes to a collection oftrips. Existing TA algorithms often do not adequately consider realtimetraffic conditions, resulting in inefficient route assignments.This paper introduces Metis, a coordinated, one-shot TA algorithmthat combines alternative routing with edge penalization and informedroute scoring. We conduct experiments in several cities toevaluate the performance of Metis against state-of-the-art oneshotmethods. Compared to the best baseline, Metis significantlyreduces CO2 emissions by 18% in Milan, 28% in Florence, and 46%in Rome, improving trip distribution considerably while still havinglow computational time. Our study proposes Metis as a promisingsolution for optimizing TA and urban transportation systems.DOI: 10.1145/3589132.3625637
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
Connected vehicle simulation framework for parking occupancy prediction (demo paper)
Resce P, Vorwerk L, Han Z, Cornacchia G, Alamdari Oi, 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.DOI: 10.1145/3557915.3560995
Project(s): HumanE-AI-Net
Metrics:
Resce P, Vorwerk L, Han Z, Cornacchia G, Alamdari Oi, 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.DOI: 10.1145/3557915.3560995
Project(s): HumanE-AI-Net
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
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, vol. 184, pp. 258-265
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, vol. 184, pp. 258-265
DOI: 10.1016/j.procs.2021.03.035
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | ISTI Repository | www.sciencedirect.com | CNR IRIS
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, vol. 10 (issue 9)
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, vol. 10 (issue 9)
DOI: 10.3390/ijgi10090599
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2020
Other
Metadata Only Access
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 IRIS