2023
Conference article
Open Access
Geolet: an interpretable model for trajectory classification
Landi C., Spinnato F., Guidotti R., Monreale A., Nanni M.
The large and diverse availability of mobility data enables the development of predictive models capable of recognizing various types of movements. Through a variety of GPS devices, any moving entity, animal, person, or vehicle can generate spatio-temporal trajectories. This data is used to infer migration patterns, manage traffic in large cities, and monitor the spread and impact of diseases, all critical situations that necessitate a thorough understanding of the underlying problem. Researchers, businesses, and governments use mobility data to make decisions that affect people's lives in many ways, employing accurate but opaque deep learning models that are difficult to interpret from a human standpoint. To address these limitations, we propose Geolet, a human-interpretable machine-learning model for trajectory classification. We use discriminative sub-trajectories extracted from mobility data to turn trajectories into a simplified representation that can be used as input by any machine learning classifier. We test our approach against state-of-the-art competitors on real-world datasets. Geolet outperforms black-box models in terms of accuracy while being orders of magnitude faster than its interpretable competitors.Source: IDA 2023 - 21st Symposium on Intelligent Data Analysis, pp. 236–248, Louvain-la-Neuve, Belgium, 12-14/04/2023
DOI: 10.1007/978-3-031-30047-9_19
Project(s): TAILOR
, XAI , SoBigData-PlusPlus , Humane AI
Metrics:
Landi C., Spinnato F., Guidotti R., Monreale A., Nanni M.
The large and diverse availability of mobility data enables the development of predictive models capable of recognizing various types of movements. Through a variety of GPS devices, any moving entity, animal, person, or vehicle can generate spatio-temporal trajectories. This data is used to infer migration patterns, manage traffic in large cities, and monitor the spread and impact of diseases, all critical situations that necessitate a thorough understanding of the underlying problem. Researchers, businesses, and governments use mobility data to make decisions that affect people's lives in many ways, employing accurate but opaque deep learning models that are difficult to interpret from a human standpoint. To address these limitations, we propose Geolet, a human-interpretable machine-learning model for trajectory classification. We use discriminative sub-trajectories extracted from mobility data to turn trajectories into a simplified representation that can be used as input by any machine learning classifier. We test our approach against state-of-the-art competitors on real-world datasets. Geolet outperforms black-box models in terms of accuracy while being orders of magnitude faster than its interpretable competitors.Source: IDA 2023 - 21st Symposium on Intelligent Data Analysis, pp. 236–248, Louvain-la-Neuve, Belgium, 12-14/04/2023
DOI: 10.1007/978-3-031-30047-9_19
Project(s): TAILOR
, XAI , SoBigData-PlusPlus , Humane AI Metrics:
See at:
ISTI Repository 
| doi.org 
| link.springer.com | CNR ExploRA
2023
Conference article
Open Access
The effects of route randomization on urban emissions
Cornacchia G., Nanni M., Pedreschi D., Pappalardo L.
Routing algorithms typically suggest the fastest path or slight variation to reach a user's desired destination. Although this suggestion at the individual level is undoubtedly advantageous for the user, from a collective point of view, the aggregation of all single suggested paths may result in an increasing impact (e.g., in terms of emissions). In this study, we use SUMO to simulate the effects of incorporating randomness into routing algorithms on emissions, their distribution, and travel time in the urban area of Milan (Italy). Our results reveal that, given the common practice of routing towards the fastest path, a certain level of randomness in routes reduces emissions and travel time. In other words, the stronger the random component in the routes, the more pronounced the benefits upon a certain threshold. Our research provides insight into the potential advantages of considering collective outcomes in routing decisions and highlights the need to explore further the relationship between route randomization and sustainability in urban transportation.Source: SUMO User Conference 2023, pp. 75–87, Berlin, Germany, 02-04/05/2023
DOI: 10.52825/scp.v4i.217
Project(s): SoBigData-PlusPlus
Metrics:
Cornacchia G., Nanni M., Pedreschi D., Pappalardo L.
Routing algorithms typically suggest the fastest path or slight variation to reach a user's desired destination. Although this suggestion at the individual level is undoubtedly advantageous for the user, from a collective point of view, the aggregation of all single suggested paths may result in an increasing impact (e.g., in terms of emissions). In this study, we use SUMO to simulate the effects of incorporating randomness into routing algorithms on emissions, their distribution, and travel time in the urban area of Milan (Italy). Our results reveal that, given the common practice of routing towards the fastest path, a certain level of randomness in routes reduces emissions and travel time. In other words, the stronger the random component in the routes, the more pronounced the benefits upon a certain threshold. Our research provides insight into the potential advantages of considering collective outcomes in routing decisions and highlights the need to explore further the relationship between route randomization and sustainability in urban transportation.Source: SUMO User Conference 2023, pp. 75–87, Berlin, Germany, 02-04/05/2023
DOI: 10.52825/scp.v4i.217
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | www.tib-op.org | CNR ExploRA
2023
Journal article
Open Access
Understanding any time series classifier with a subsequence-based explainer
Spinnato F., Guidotti R., Monreale A., Nanni M., Pedreschi D., Giannotti F.
The growing availability of time series data has increased the usage of classifiers for this data type. Unfortunately, state-of-the-art time series classifiers are black-box models and, therefore, not usable in critical domains such as healthcare or finance, where explainability can be a crucial requirement. This paper presents a framework to explain the predictions of any black-box classifier for univariate and multivariate time series. The provided explanation is composed of three parts. First, a saliency map highlighting the most important parts of the time series for the classification. Second, an instance-based explanation exemplifies the blackbox's decision by providing a set of prototypical and counterfactual time series. Third, a factual and counterfactual rule-based explanation, revealing the reasons for the classification through logical conditions based on subsequences that must, or must not, be contained in the time series. Experiments and benchmarks show that the proposed method provides faithful, meaningful, stable, and interpretable explanations.Source: ACM transactions on knowledge discovery from data 18 (2023): 1–34. doi:10.1145/3624480
DOI: 10.1145/3624480
Project(s): TAILOR, XAI , SoBigData-PlusPlus
Metrics:
Spinnato F., Guidotti R., Monreale A., Nanni M., Pedreschi D., Giannotti F.
The growing availability of time series data has increased the usage of classifiers for this data type. Unfortunately, state-of-the-art time series classifiers are black-box models and, therefore, not usable in critical domains such as healthcare or finance, where explainability can be a crucial requirement. This paper presents a framework to explain the predictions of any black-box classifier for univariate and multivariate time series. The provided explanation is composed of three parts. First, a saliency map highlighting the most important parts of the time series for the classification. Second, an instance-based explanation exemplifies the blackbox's decision by providing a set of prototypical and counterfactual time series. Third, a factual and counterfactual rule-based explanation, revealing the reasons for the classification through logical conditions based on subsequences that must, or must not, be contained in the time series. Experiments and benchmarks show that the proposed method provides faithful, meaningful, stable, and interpretable explanations.Source: ACM transactions on knowledge discovery from data 18 (2023): 1–34. doi:10.1145/3624480
DOI: 10.1145/3624480
Project(s): TAILOR
, XAI , SoBigData-PlusPlus Metrics:
See at:
dl.acm.org | ISTI Repository | ACM Transactions on Knowledge Discovery from Data | CNR ExploRA
2023
Conference article
Open Access
Human mobility, AI assistants, and urban emissions: an insidious triangle
Pappalardo L., Bohm M., Cornacchia G., Mauro G., Pedreschi D., Nanni M.
Transportation remains a significant contributor to greenhouse gas emissions, with a substantial proportion originating from road transport and passenger travel in particular. Today, the relationship between transportation and urban emissions is even more complex, given the increasingly prevalent role and the pervasiveness of AI-based GPS navigation systems such as Google Maps and TomTom. While these services offer benefits to individual drivers, they can also exacerbate congestion and increase pollution if too many drivers are directed onto the same route. In this article, we provide two examples from our research group that explore the impact of vehicular transportation and mobility-AI-based applications on urban emissions. By conducting realistic simulations and studying the impact of GPS navigation systems on emissions, we provide insights into the potential for mitigating transportation emissions and developing policies that promote sustainable urban mobility. Our examples demonstrate how vehicle-generated emissions can be reduced and how studying the impact of GPS navigation systems on emissions can lead to unexpected findings. Overall, our analysis suggests that it is crucial to consider the impact of emerging technologies on transportation and emissions, and to develop strategies that promote sustainable mobility while ensuring the optimal use of these tools.Source: Ital-IA 2023: 3rd National Conference on Artificial Intelligence, pp. 585–589, Pisa, Italy, 29-31/05/2023
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Pappalardo L., Bohm M., Cornacchia G., Mauro G., Pedreschi D., Nanni M.
Transportation remains a significant contributor to greenhouse gas emissions, with a substantial proportion originating from road transport and passenger travel in particular. Today, the relationship between transportation and urban emissions is even more complex, given the increasingly prevalent role and the pervasiveness of AI-based GPS navigation systems such as Google Maps and TomTom. While these services offer benefits to individual drivers, they can also exacerbate congestion and increase pollution if too many drivers are directed onto the same route. In this article, we provide two examples from our research group that explore the impact of vehicular transportation and mobility-AI-based applications on urban emissions. By conducting realistic simulations and studying the impact of GPS navigation systems on emissions, we provide insights into the potential for mitigating transportation emissions and developing policies that promote sustainable urban mobility. Our examples demonstrate how vehicle-generated emissions can be reduced and how studying the impact of GPS navigation systems on emissions can lead to unexpected findings. Overall, our analysis suggests that it is crucial to consider the impact of emerging technologies on transportation and emissions, and to develop strategies that promote sustainable mobility while ensuring the optimal use of these tools.Source: Ital-IA 2023: 3rd National Conference on Artificial Intelligence, pp. 585–589, Pisa, Italy, 29-31/05/2023
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Restricted
Interpretable data partitioning through tree-based clustering methods
Guidotti R., Landi C., Beretta A., Fadda D., Nanni M.
Interpretable Data Partitioning Through Tree-Based Clustering Methods Riccardo Guidotti, Cristiano Landi, Andrea Beretta, Daniele Fadda & Mirco Nanni Conference paper First Online: 08 October 2023 311 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14276) The growing interpretable machine learning research field is mainly focusing on the explanation of supervised approaches. However, also unsupervised approaches might benefit from considering interpretability aspects. While existing clustering methods only provide the assignment of records to clusters without justifying the partitioning, we propose tree-based clustering methods that offer interpretable data partitioning through a shallow decision tree. These decision trees enable easy-to-understand explanations of cluster assignments through short and understandable split conditions. The proposed methods are evaluated through experiments on synthetic and real datasets and proved to be more effective than traditional clustering approaches and interpretable ones in terms of standard evaluation measures and runtime. Finally, a case study involving human participation demonstrates the effectiveness of the interpretable clustering trees returned by the proposed method.Source: DS 2023 - 26th International Conference on Discovery Science, pp. 492–507, Porto, Portugal, 09-11/10/2023
DOI: 10.1007/978-3-031-45275-8_33
Metrics:
Guidotti R., Landi C., Beretta A., Fadda D., Nanni M.
Interpretable Data Partitioning Through Tree-Based Clustering Methods Riccardo Guidotti, Cristiano Landi, Andrea Beretta, Daniele Fadda & Mirco Nanni Conference paper First Online: 08 October 2023 311 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14276) The growing interpretable machine learning research field is mainly focusing on the explanation of supervised approaches. However, also unsupervised approaches might benefit from considering interpretability aspects. While existing clustering methods only provide the assignment of records to clusters without justifying the partitioning, we propose tree-based clustering methods that offer interpretable data partitioning through a shallow decision tree. These decision trees enable easy-to-understand explanations of cluster assignments through short and understandable split conditions. The proposed methods are evaluated through experiments on synthetic and real datasets and proved to be more effective than traditional clustering approaches and interpretable ones in terms of standard evaluation measures and runtime. Finally, a case study involving human participation demonstrates the effectiveness of the interpretable clustering trees returned by the proposed method.Source: DS 2023 - 26th International Conference on Discovery Science, pp. 492–507, Porto, Portugal, 09-11/10/2023
DOI: 10.1007/978-3-031-45275-8_33
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2023
Conference article
Open Access
TrajParquet: a trajectory-oriented column file format for mobility data lakes
Koutroumanis N., Doulkeridis C., Renso C., Nanni M., Perego R.
Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, pp. 73:1–73:4, 13-16/11/2023
DOI: 10.1145/3589132.3625623
Project(s): SoBigData-PlusPlus
Metrics:
Koutroumanis N., Doulkeridis C., Renso C., Nanni M., Perego R.
Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, pp. 73:1–73:4, 13-16/11/2023
DOI: 10.1145/3589132.3625623
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
The trajectory interval forest classifier for trajectory classification
Landi C., Guidotti R., Nanni M., Monreale A.
GPS devices generate spatio-temporal trajectories for different types of moving objects. Scientists can exploit them to analyze migration patterns, manage city traffic, monitor the spread of diseases, etc. Many current state-of-the-art models that use this data type require a not negligible running time to be trained. To overcome this issue, we propose the Trajectory Interval Forest (TIF) classifier, an efficient model with high throughput. TIF works by calculating various mobility-related statistics over a set of randomly selected intervals. These statistics are used to create a tabular representation of the data, which can be used as input for any classical classifier. Our results show that TIF is comparable to or better than state-of-art in terms of accuracy and is orders of magnitude faster.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3625617
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Metrics:
Landi C., Guidotti R., Nanni M., Monreale A.
GPS devices generate spatio-temporal trajectories for different types of moving objects. Scientists can exploit them to analyze migration patterns, manage city traffic, monitor the spread of diseases, etc. Many current state-of-the-art models that use this data type require a not negligible running time to be trained. To overcome this issue, we propose the Trajectory Interval Forest (TIF) classifier, an efficient model with high throughput. TIF works by calculating various mobility-related statistics over a set of randomly selected intervals. These statistics are used to create a tabular representation of the data, which can be used as input for any classical classifier. Our results show that TIF is comparable to or better than state-of-art in terms of accuracy and is orders of magnitude faster.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3625617
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
dl.acm.org | ISTI Repository | CNR ExploRA
2023
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 (2023). doi:10.1142/S0219477524500160
DOI: 10.1142/s0219477524500160
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 (2023). doi:10.1142/S0219477524500160
DOI: 10.1142/s0219477524500160
Metrics:
See at:
ISTI Repository | Fluctuation and Noise Letters | CNR ExploRA
2023
Journal article
Open Access
From fossil fuel to electricity: studying the impact of EVs on the daily mobility life of users
Nanni M., Alamdari O. I., Bonavita A., Cintia P.
Electric mobility is one of the main advocated solutions for making urban environments ecologically more sustainable, improving the quality of life of citizens. Despite the quick development of the Electric Vehicle (EV) market and the strong commitment of car makers, various social barriers need still to be overcome to complete the transition of mobility towards electric. Indeed, most users are very little familiar with what driving an EV really means and what it might change in their daily lives if they replace their fuel-based vehicle with an electric one. This lack of knowledge causes several worries to the average potential user, even though its many advantages for the environment are clear.Source: IEEE transactions on intelligent transportation systems (Online) (2023): 1–11. doi:10.1109/TITS.2023.3340742
DOI: 10.1109/tits.2023.3340742
Project(s): Track and Know
Metrics:
Nanni M., Alamdari O. I., Bonavita A., Cintia P.
Electric mobility is one of the main advocated solutions for making urban environments ecologically more sustainable, improving the quality of life of citizens. Despite the quick development of the Electric Vehicle (EV) market and the strong commitment of car makers, various social barriers need still to be overcome to complete the transition of mobility towards electric. Indeed, most users are very little familiar with what driving an EV really means and what it might change in their daily lives if they replace their fuel-based vehicle with an electric one. This lack of knowledge causes several worries to the average potential user, even though its many advantages for the environment are clear.Source: IEEE transactions on intelligent transportation systems (Online) (2023): 1–11. doi:10.1109/TITS.2023.3340742
DOI: 10.1109/tits.2023.3340742
Project(s): Track and Know
Metrics:
See at:
ieeexplore.ieee.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
One-shot traffic assignment with forward-looking penalization
Cornacchia G., Nanni M., Pappalardo L.
Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider realtime traffic conditions, resulting in inefficient route assignments. This paper introduces Metis, a coordinated, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of Metis against state-of-the-art oneshot methods. Compared to the best baseline, Metis significantly reduces CO2 emissions by 18% in Milan, 28% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes Metis as a promising solution for optimizing TA and urban transportation systems.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3625637
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Metrics:
Cornacchia G., Nanni M., Pappalardo L.
Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider realtime traffic conditions, resulting in inefficient route assignments. This paper introduces Metis, a coordinated, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of Metis against state-of-the-art oneshot methods. Compared to the best baseline, Metis significantly reduces CO2 emissions by 18% in Milan, 28% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes Metis as a promising solution for optimizing TA and urban transportation systems.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3625637
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
dl.acm.org | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
City indicators for geographical transfer learning: an application to crash prediction
Nanni M., Guidotti R., Bonavita A., Alamdari O. I.
The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users' mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution.Source: Geoinformatica (Dordrecht) (2022). doi:10.1007/s10707-022-00464-3
DOI: 10.1007/s10707-022-00464-3
Project(s): Track and Know, Track and Know , SoBigData-PlusPlus
Metrics:
Nanni M., Guidotti R., Bonavita A., Alamdari O. I.
The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users' mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution.Source: Geoinformatica (Dordrecht) (2022). doi:10.1007/s10707-022-00464-3
DOI: 10.1007/s10707-022-00464-3
Project(s): Track and Know
, Track and Know , SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | GeoInformatica | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
Gross polluters and vehicle emissions reduction
Bohm M., Nanni M., Pappalardo L.
Vehicle emissions produce an important share of a city's air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing the full driving cycle of vehicles, or focus on a few vehicles. We have used GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in three European cities. We found that the emissions across the vehicles and roads are well approximated by heavy-tailed distributions and thus discovered the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are far more effective than those limiting circulation based on an uninformed choice of vehicles. Our study contributes to shaping the discussion on how to measure emissions with digital data.Source: Nature sustainability (2022). doi:10.1038/s41893-022-00903-x
DOI: 10.1038/s41893-022-00903-x
Project(s): Track and Know, HumanE-AI-Net , SoBigData-PlusPlus
Metrics:
Bohm M., Nanni M., Pappalardo L.
Vehicle emissions produce an important share of a city's air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing the full driving cycle of vehicles, or focus on a few vehicles. We have used GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in three European cities. We found that the emissions across the vehicles and roads are well approximated by heavy-tailed distributions and thus discovered the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are far more effective than those limiting circulation based on an uninformed choice of vehicles. Our study contributes to shaping the discussion on how to measure emissions with digital data.Source: Nature sustainability (2022). doi:10.1038/s41893-022-00903-x
DOI: 10.1038/s41893-022-00903-x
Project(s): Track and Know
, HumanE-AI-Net , SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | www.nature.com | CNR ExploRA
2022
Conference article
Open Access
Explaining crash predictions on multivariate time series data
Spinnato F., Guidotti R., Nanni M., Maccagnola D., Paciello G., Farina A. B.
In Assicurazioni Generali, an automatic decision-making model is used to check real-time multivariate time series and alert if a car crash happened. In such a way, a Generali operator can call the customer to provide first assistance. The high sensitivity of the model used, combined with the fact that the model is not interpretable, might cause the operator to call customers even though a car crash did not happen but only due to a harsh deviation or the fact that the road is bumpy. Our goal is to tackle the problem of interpretability for car crash prediction and propose an eXplainable Artificial Intelligence (XAI) workflow that allows gaining insights regarding the logic behind the deep learning predictive model adopted by Generali. We reach our goal by building an interpretable alternative to the current obscure model that also reduces the training data usage and the prediction time.Source: DS 2022 - 25th International Conference on Discovery Science, pp. 556–566, Montpellier, France, 10-12/10/2022
DOI: 10.1007/978-3-031-18840-4_39
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Metrics:
Spinnato F., Guidotti R., Nanni M., Maccagnola D., Paciello G., Farina A. B.
In Assicurazioni Generali, an automatic decision-making model is used to check real-time multivariate time series and alert if a car crash happened. In such a way, a Generali operator can call the customer to provide first assistance. The high sensitivity of the model used, combined with the fact that the model is not interpretable, might cause the operator to call customers even though a car crash did not happen but only due to a harsh deviation or the fact that the road is bumpy. Our goal is to tackle the problem of interpretability for car crash prediction and propose an eXplainable Artificial Intelligence (XAI) workflow that allows gaining insights regarding the logic behind the deep learning predictive model adopted by Generali. We reach our goal by building an interpretable alternative to the current obscure model that also reduces the training data usage and the prediction time.Source: DS 2022 - 25th International Conference on Discovery Science, pp. 556–566, Montpellier, France, 10-12/10/2022
DOI: 10.1007/978-3-031-18840-4_39
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2022
Conference article
Open Access
Connected vehicle simulation framework for parking occupancy prediction (demo paper)
Resce P., Vorwerk L., Han Z., Cornacchia G., Alamdari O. I., Nanni M., Pappalardo L., Weimer D., Liu Y.
This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022
DOI: 10.1145/3557915.3560995
Project(s): HumanE-AI-Net
Metrics:
Resce P., Vorwerk L., Han Z., Cornacchia G., Alamdari O. I., Nanni M., Pappalardo L., Weimer D., Liu Y.
This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022
DOI: 10.1145/3557915.3560995
Project(s): HumanE-AI-Net
Metrics:
See at:
ISTI Repository | dl.acm.org | doi.org | CNR ExploRA
2022
Conference article
Open Access
How routing strategies impact urban emissions
Cornacchia G., Bohm M., Mauro G., Nanni M., Pedreschi D., Pappalardo L.
Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022
DOI: 10.1145/3557915.3560977
DOI: 10.48550/arxiv.2207.01456
Project(s): SoBigData-PlusPlus
Metrics:
Cornacchia G., Bohm M., Mauro G., Nanni M., Pedreschi D., Pappalardo L.
Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022
DOI: 10.1145/3557915.3560977
DOI: 10.48550/arxiv.2207.01456
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | ISTI Repository | dl.acm.org | doi.org | doi.org | CNR ExploRA
2021
Conference article
Open Access
Measuring immigrants adoption of natives shopping consumption with machine learning
Guidotti R., Nanni M., Giannotti F., Pedreschi D., Bertoli S., Speciale B., Rapoport H.
Tell me what you eat and I will tell you what you are". Jean Anthelme Brillat-Savarin was among the firsts to recognize the relationship between identity and food consumption. Food adoption choices are much less exposed to external judgment and social pressure than other individual behaviours, and can be observed over a long period. That makes them an interesting basis for, among other applications, studying the integration of immigrants from a food consumption viewpoint. Indeed, in this work we analyze immigrants' food consumption from shopping retail data for understanding if and how it converges towards those of natives. As core contribution of our proposal, we define a score of adoption of natives' consumption habits by an individual as the probability of being recognized as a native from a machine learning classifier, thus adopting a completely data-driven approach. We measure the immigrant's adoption of natives' consumption behavior over a long time, and we identify different trends. A case study on real data of a large nation-wide supermarket chain reveals that we can distinguish five main different groups of immigrants depending on their trends of native consumption adoption.Source: ECML PKDD 2020 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 369–385, Ghent, Belgium, September 14-18, 2020
DOI: 10.1007/978-3-030-67670-4_23
Metrics:
Guidotti R., Nanni M., Giannotti F., Pedreschi D., Bertoli S., Speciale B., Rapoport H.
Tell me what you eat and I will tell you what you are". Jean Anthelme Brillat-Savarin was among the firsts to recognize the relationship between identity and food consumption. Food adoption choices are much less exposed to external judgment and social pressure than other individual behaviours, and can be observed over a long period. That makes them an interesting basis for, among other applications, studying the integration of immigrants from a food consumption viewpoint. Indeed, in this work we analyze immigrants' food consumption from shopping retail data for understanding if and how it converges towards those of natives. As core contribution of our proposal, we define a score of adoption of natives' consumption habits by an individual as the probability of being recognized as a native from a machine learning classifier, thus adopting a completely data-driven approach. We measure the immigrant's adoption of natives' consumption behavior over a long time, and we identify different trends. A case study on real data of a large nation-wide supermarket chain reveals that we can distinguish five main different groups of immigrants depending on their trends of native consumption adoption.Source: ECML PKDD 2020 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 369–385, Ghent, Belgium, September 14-18, 2020
DOI: 10.1007/978-3-030-67670-4_23
Metrics:
See at:
ISTI Repository | link.springer.com | link.springer.com | CNR ExploRA
2021
Report
Open Access
Improving vehicles' emissions reduction policies by targeting gross polluters
Böhm M., Nanni M., Pappalardo L.
Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. This study uses GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of vehicles in three European cities. We discover the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study applies to any city and may contribute to shaping the discussion on how to measure emissions with digital data.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus
Böhm M., Nanni M., Pappalardo L.
Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. This study uses GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of vehicles in three European cities. We discover the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study applies to any city and may contribute to shaping the discussion on how to measure emissions with digital data.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2021
Conference article
Open Access
Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution
Böhm M., Nanni M., Pappalardo L.
Monitoring air pollution plays a key role when trying to reduce its impact on the environment and on human health. Traditionally, two main sources of information about the quantity of pollutants over a city are used: monitoring stations at ground level (when available), and satellites' remote sensing. In addition to these two, other methods have been developed in the last years that aim at understanding how traffic emissions behave in space and time at a finer scale, taking into account the human mobility patterns. We present a simple and versatile framework for estimating the quantity of four air pollutants (CO2, NOx, PM, VOC) emitted by private vehicles moving on a road network, starting from raw GPS traces and information about vehicles' fuel type, and use this framework for analyses on how such pollutants distribute over the road networks of different cities.Source: NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Online conference, 11/12/2020
Project(s): SoBigData-PlusPlus
Böhm M., Nanni M., Pappalardo L.
Monitoring air pollution plays a key role when trying to reduce its impact on the environment and on human health. Traditionally, two main sources of information about the quantity of pollutants over a city are used: monitoring stations at ground level (when available), and satellites' remote sensing. In addition to these two, other methods have been developed in the last years that aim at understanding how traffic emissions behave in space and time at a finer scale, taking into account the human mobility patterns. We present a simple and versatile framework for estimating the quantity of four air pollutants (CO2, NOx, PM, VOC) emitted by private vehicles moving on a road network, starting from raw GPS traces and information about vehicles' fuel type, and use this framework for analyses on how such pollutants distribute over the road networks of different cities.Source: NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Online conference, 11/12/2020
Project(s): SoBigData-PlusPlus
See at:
ISTI Repository | www.climatechange.ai | CNR ExploRA
2021
Journal article
Open Access
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni M., Andrienko G., Barabasi A. -L., Boldrini C., Bonchi F., Cattuto C., Chiaromonte F., Comande G., Conti M., Cote M., Dignum F., Dignum V., Domingo-Ferrer J., Ferragina P., Giannotti F., Guidotti R., Helbing D., Kaski K., Kertesz J., Lehmann S., Lepri B., Lukowicz P., Matwin S., Jimenez D. M., Monreale A., Morik K., Oliver N., Passarella A., Passerini A., Pedreschi D., Pentland A., Pianesi F., Pratesi F., Rinzivillo S., Ruggieri S., Siebes A., Torra V., Trasarti R., Hoven J., Vespignani A.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates--if and when they want and for specific aims--with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: Ethics and information technology 23 (2021). doi:10.1007/s10676-020-09572-w
DOI: 10.1007/s10676-020-09572-w
Project(s): SoBigData-PlusPlus
Metrics:
Nanni M., Andrienko G., Barabasi A. -L., Boldrini C., Bonchi F., Cattuto C., Chiaromonte F., Comande G., Conti M., Cote M., Dignum F., Dignum V., Domingo-Ferrer J., Ferragina P., Giannotti F., Guidotti R., Helbing D., Kaski K., Kertesz J., Lehmann S., Lepri B., Lukowicz P., Matwin S., Jimenez D. M., Monreale A., Morik K., Oliver N., Passarella A., Passerini A., Pedreschi D., Pentland A., Pianesi F., Pratesi F., Rinzivillo S., Ruggieri S., Siebes A., Torra V., Trasarti R., Hoven J., Vespignani A.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates--if and when they want and for specific aims--with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: Ethics and information technology 23 (2021). doi:10.1007/s10676-020-09572-w
DOI: 10.1007/s10676-020-09572-w
Project(s): SoBigData-PlusPlus
Metrics:
See at:
Aaltodoc Publication Archive | Aaltodoc Publication Archive | Ethics and Information Technology | Ethics and Information Technology | Recolector de Ciencia Abierta, RECOLECTA | Archivio Istituzionale | link.springer.com | Ethics and Information Technology | City Research Online | ISTI Repository | Online Research Database In Technology | NARCIS | NARCIS | Digitala Vetenskapliga Arkivet - Academic Archive On-line | Publikationer från Umeå universitet | NARCIS | kclpure.kcl.ac.uk | Fraunhofer-ePrints | Fraunhofer-ePrints | publons.com | www.scopus.com | CNR ExploRA
2021
Journal article
Open Access
Individual and collective stop-based adaptive trajectory segmentation
Bonavita A., Guidotti R., Nanni M.
Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results.Source: Geoinformatica (Dordrecht) (2021). doi:10.1007/s10707-021-00449-8
DOI: 10.1007/s10707-021-00449-8
Project(s): Track and Know
Metrics:
Bonavita A., Guidotti R., Nanni M.
Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results.Source: Geoinformatica (Dordrecht) (2021). doi:10.1007/s10707-021-00449-8
DOI: 10.1007/s10707-021-00449-8
Project(s): Track and Know
Metrics:
See at:
link.springer.com | GeoInformatica | GeoInformatica | ISTI Repository | ISTI Repository | CNR ExploRA