2025
Conference article
Metadata Only Access
Urban Region Embeddings from Service-Specific Mobile Traffic Data
Loddi Giulio, Pugliese Chiara, Lettich Francesco, Pinelli Fabio, Renso Chiara
With the advent of modern 4G/5G networks, mobile phone data collected by operators now includes detailed, servicespecific traffic information with high spatio-temporal resolution. In this paper, we explore the potential of such data for learning high-quality embeddings (representations) of urban regions. We propose a methodology that takes this data as input and employs a temporal convolutional network-based autoencoder, transformers, and learnable weighted sum models to extract key urban features. In the experimental evaluation, conducted using realworld datasets, we demonstrate that the embeddings generated by our methodology effectively capture urban characteristics. In particular, our embeddings are compared against those of a state-of-the-art multi-modal competitor across two downstream tasks, showing comparable quality. In general, our work highlights the potential and utility of service-specific mobile traffic data for urban research and the importance of making this data accessible to foster public innovation.DOI: 10.1109/mdm65600.2025.00028
Project(s): MUSIT
, RESearch and innovation on future Telecommunications systems and networks, to make Italy more smART, Spoke 1 ”Human-centered AI” of the M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - ”FAIR - Future Artificial Intelligence Research”
Metrics:
Loddi Giulio, Pugliese Chiara, Lettich Francesco, Pinelli Fabio, Renso Chiara
With the advent of modern 4G/5G networks, mobile phone data collected by operators now includes detailed, servicespecific traffic information with high spatio-temporal resolution. In this paper, we explore the potential of such data for learning high-quality embeddings (representations) of urban regions. We propose a methodology that takes this data as input and employs a temporal convolutional network-based autoencoder, transformers, and learnable weighted sum models to extract key urban features. In the experimental evaluation, conducted using realworld datasets, we demonstrate that the embeddings generated by our methodology effectively capture urban characteristics. In particular, our embeddings are compared against those of a state-of-the-art multi-modal competitor across two downstream tasks, showing comparable quality. In general, our work highlights the potential and utility of service-specific mobile traffic data for urban research and the importance of making this data accessible to foster public innovation.DOI: 10.1109/mdm65600.2025.00028
Project(s): MUSIT
, RESearch and innovation on future Telecommunications systems and networks, to make Italy more smART, Spoke 1 ”Human-centered AI” of the M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - ”FAIR - Future Artificial Intelligence Research”Metrics:
See at:
CNR IRIS 
2024
Conference article
Restricted
Understanding human mobility dynamics: insights from summarized semantic trajectories
Pugliese C., Lettich F., Pinelli F., Renso C.
Mobility data analysis provides insights into human movement patterns, traffic flows, and urban planning strategies. Human dynamics analysis focuses on tracking people to investigate how individuals and groups behave, interact, and evolve. Various mobility data sources, such as GPS, mobile phone records, social media, and transportation logs, are often semantically enriched and used for these analyses. This results in the generation of new, complex datasets that require effective summarization methods to reduce data volume while preserving relevant information. In this work, we aim to demonstrate the effective use of summarized semantic trajectories in analyzing human mobility behaviours. We offer empirical evidence from a case study, showing how this type of trajectory helps in understanding human mobility, especially in distinguishing between routine and non-routine behaviours. Experimental results show that the analysis results are comparable with the results obtained in the original (non summarized) dataset.DOI: 10.1109/mdm61037.2024.00039
Project(s): CAMEO, PRIN 2022 n. 2022ZLL7MW, SoBigData-PlusPlus, Spoke 1 ”Human-centered AI” of the M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - ”FAIR - Future Artificial Intelligence Research”
Metrics:
Pugliese C., Lettich F., Pinelli F., Renso C.
Mobility data analysis provides insights into human movement patterns, traffic flows, and urban planning strategies. Human dynamics analysis focuses on tracking people to investigate how individuals and groups behave, interact, and evolve. Various mobility data sources, such as GPS, mobile phone records, social media, and transportation logs, are often semantically enriched and used for these analyses. This results in the generation of new, complex datasets that require effective summarization methods to reduce data volume while preserving relevant information. In this work, we aim to demonstrate the effective use of summarized semantic trajectories in analyzing human mobility behaviours. We offer empirical evidence from a case study, showing how this type of trajectory helps in understanding human mobility, especially in distinguishing between routine and non-routine behaviours. Experimental results show that the analysis results are comparable with the results obtained in the original (non summarized) dataset.DOI: 10.1109/mdm61037.2024.00039
Project(s): CAMEO, PRIN 2022 n. 2022ZLL7MW, SoBigData-PlusPlus
, Spoke 1 ”Human-centered AI” of the M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - ”FAIR - Future Artificial Intelligence Research”Metrics:
2024
Conference article
Restricted
UltraMovelets: efficient movelet extraction for multiple aspect trajectory classification
Portela T. T., Machado V. L., Carvalho J. T., Bogorny V., Bernasconi A., Renso C.
Several methods for trajectory classification build models exploring trajectory global features, such as the average and the standard deviation of speed and acceleration, but for some applications these features may not be the best to determine the class. Other works explore local features, applying trajectory partition and discretization, that lose important movement information that could discriminate the class. In this work we propose a new method, called Movelets, to discover relevant subtrajectories without the need of a predefined criteria for either trajectory partition or discretization. We extend the concept of time series shapelets for trajectories, and to the best of our knowledge, this work is the first to use shapelets in the trajectory domain. We evaluated the proposed approach with several categories of datasets, including hurricanes, vehicles, animals, and transportation means, and show with extensive experiments that our method largely outperformed state of the art works, indicating that Movelets is very promising for trajectory classification.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 14911, pp. 79-94. Naples, Italy, 26–28/08/2024
DOI: 10.1007/978-3-031-68312-1_6
Project(s): MASTER
Metrics:
Portela T. T., Machado V. L., Carvalho J. T., Bogorny V., Bernasconi A., Renso C.
Several methods for trajectory classification build models exploring trajectory global features, such as the average and the standard deviation of speed and acceleration, but for some applications these features may not be the best to determine the class. Other works explore local features, applying trajectory partition and discretization, that lose important movement information that could discriminate the class. In this work we propose a new method, called Movelets, to discover relevant subtrajectories without the need of a predefined criteria for either trajectory partition or discretization. We extend the concept of time series shapelets for trajectories, and to the best of our knowledge, this work is the first to use shapelets in the trajectory domain. We evaluated the proposed approach with several categories of datasets, including hurricanes, vehicles, animals, and transportation means, and show with extensive experiments that our method largely outperformed state of the art works, indicating that Movelets is very promising for trajectory classification.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 14911, pp. 79-94. Naples, Italy, 26–28/08/2024
DOI: 10.1007/978-3-031-68312-1_6
Project(s): MASTER
Metrics:
See at:
doi.org | Archivio della Ricerca - Università di Pisa | IRIS Cnr | CNR IRIS | IRIS Cnr
2024
Journal article
Open Access
TrajectGuard: a comprehensive privacy-risk framework for multiple-aspects trajectories
Gomes F. O., Pellungrini R., Monreale A., Renso C., Martina J. E.
With the rise of the Internet of Things (IoT), social networks, and mobile devices, vast amounts of mobility data are continuously generated. These data encompass diverse location information from various sources, including smart vehicles, sensors, wearables, and social media platforms. By leveraging these data, we explore the semantic enrichment of trajectory components related to moving objects and locations, bringing the so-called multiple-aspects trajectories and relative privacy issues. Privacy risk analysis is crucial for the earlier detection of privacy problems, particularly when dealing with semantically enriched trajectories. In this study, we introduced the TrajectGuard privacy risk assessment framework. TrajectGuard, an extension of PRUDEnce, achieved significant results by formulating and assessing the privacy risk of multiple-aspects trajectories under several proposed attacks. The framework introduced a nuanced risk evaluation using AspectGuard and conducted fair privacy assessments on anonymized datasets using AnonimoGuard. Its adaptability and versatility make TrajectGuard a valuable tool for preserving data privacy with multiple-aspects.Source: IEEE ACCESS, vol. 12, pp. 136354-136378
DOI: 10.1109/access.2024.3462088
Metrics:
Gomes F. O., Pellungrini R., Monreale A., Renso C., Martina J. E.
With the rise of the Internet of Things (IoT), social networks, and mobile devices, vast amounts of mobility data are continuously generated. These data encompass diverse location information from various sources, including smart vehicles, sensors, wearables, and social media platforms. By leveraging these data, we explore the semantic enrichment of trajectory components related to moving objects and locations, bringing the so-called multiple-aspects trajectories and relative privacy issues. Privacy risk analysis is crucial for the earlier detection of privacy problems, particularly when dealing with semantically enriched trajectories. In this study, we introduced the TrajectGuard privacy risk assessment framework. TrajectGuard, an extension of PRUDEnce, achieved significant results by formulating and assessing the privacy risk of multiple-aspects trajectories under several proposed attacks. The framework introduced a nuanced risk evaluation using AspectGuard and conducted fair privacy assessments on anonymized datasets using AnonimoGuard. Its adaptability and versatility make TrajectGuard a valuable tool for preserving data privacy with multiple-aspects.Source: IEEE ACCESS, vol. 12, pp. 136354-136378
DOI: 10.1109/access.2024.3462088
Metrics:
See at:
IEEE Access 
| Archivio della Ricerca - Università di Pisa | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR IRIS | ieeexplore.ieee.org | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Journal article
Open Access
Efficiency boosts in human mobility data privacy risk assessment: advancements within the PRUDEnce framework
Gomes F. O., Pellungrini R., Monreale A., Renso C., Martina J. E.
With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets.Source: APPLIED SCIENCES, vol. 14 (issue 17)
DOI: 10.3390/app14178014
Project(s): Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—, PNRR-M4C2-Investimento 1.3, Partenariato Esteso PE00000013-“FAIR-Future Artificial Intelligence Research”-Spoke 1 “Human-centered AI”, funded by the, SoBigData.it
Metrics:
Gomes F. O., Pellungrini R., Monreale A., Renso C., Martina J. E.
With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets.Source: APPLIED SCIENCES, vol. 14 (issue 17)
DOI: 10.3390/app14178014
Project(s): Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—, PNRR-M4C2-Investimento 1.3, Partenariato Esteso PE00000013-“FAIR-Future Artificial Intelligence Research”-Spoke 1 “Human-centered AI”, funded by the, SoBigData.it
Metrics:
See at:
Applied Sciences | Applied Sciences | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Contribution to book
Open Access
Message from the General Chairs
Renso C., Sakr M., Aref W. G.
Message from the General ChairsDOI: 10.1109/mdm61037.2024.00005
Metrics:
Renso C., Sakr M., Aref W. G.
Message from the General ChairsDOI: 10.1109/mdm61037.2024.00005
Metrics:
2023
Conference article
Open Access
A general methodology for building multiple aspect trajectories
Lettich F, Pugliese C, Renso C, Pinelli F
The massive use of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. Such data can be enriched with several semantic dimensions (or aspects), i.e., contextual and heterogeneous information captured in the surrounding environment, leading to the creation of multiple aspect trajectories (MATs). In this work, we present how the MAT-Builder system can be used for the semantic enrichment processing of movement data while being agnostic to aspects and external semantic data sources. This is achieved by integrating MAT-Builder into a methodology which encompasses three design principles and a uniform representation formalism for enriched data based on the Resource Description Framework (RDF) format. An example scenario involving the generation and querying of a dataset of MATs gives a glimpse of the possibilities that our methodology can open up.DOI: 10.1145/3555776.3577832
Project(s): MobiDataLab, MASTER , SoBigData-PlusPlus
Metrics:
Lettich F, Pugliese C, Renso C, Pinelli F
The massive use of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. Such data can be enriched with several semantic dimensions (or aspects), i.e., contextual and heterogeneous information captured in the surrounding environment, leading to the creation of multiple aspect trajectories (MATs). In this work, we present how the MAT-Builder system can be used for the semantic enrichment processing of movement data while being agnostic to aspects and external semantic data sources. This is achieved by integrating MAT-Builder into a methodology which encompasses three design principles and a uniform representation formalism for enriched data based on the Resource Description Framework (RDF) format. An example scenario involving the generation and querying of a dataset of MATs gives a glimpse of the possibilities that our methodology can open up.DOI: 10.1145/3555776.3577832
Project(s): MobiDataLab
, MASTER , SoBigData-PlusPlus Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Semantic enrichment of mobility data: a comprehensive methodology and the MAT-BUILDER system
Lettich F, Pugliese C, Renso C, Pinelli F
The widespread adoption of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. This data often needs to be enriched with a variety of semantic dimensions, or aspects, that provide contextual and heterogeneous information about the surrounding environment, resulting in the creation of multiple aspect trajectories (MATs). Common examples of aspects can be points of interest, user photos, transportation means, weather conditions, social media posts, and many more. However, the literature does not currently provide a consensus on how to semantically enrich mobility data with aspects, particularly in dynamic scenarios where semantic information is extracted from numerous and heterogeneous external data sources. In this work, we aim to address this issue by presenting a comprehensive methodology to facilitate end users in instantiating their semantic enrichment processes of movement data. The methodology is agnostic to semantic aspects and external semantic data sources. The vision behind our methodology rests on three pillars: (1) three design principles which we argue are necessary for designing systems capable of instantiating arbitrary semantic enrichment processes; (2) the MAT-Builder system, which embodies these principles; (3) the use of an RDF knowledge graph-based representation to store MATs datasets, thereby enabling uniform querying and analysis of enriched movement data. We qualitatively evaluate the methodology in two complementary example scenarios, where we show both the potential in generating interesting and useful semantically enriched mobility datasets, and the expressive power in querying the resulting RDF trajectories with SPARQL.Source: IEEE ACCESS, vol. 11, pp. 90857-90875
DOI: 10.1109/access.2023.3307824
Project(s): MobiDataLab, MASTER , SoBigData-PlusPlus
Metrics:
Lettich F, Pugliese C, Renso C, Pinelli F
The widespread adoption of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. This data often needs to be enriched with a variety of semantic dimensions, or aspects, that provide contextual and heterogeneous information about the surrounding environment, resulting in the creation of multiple aspect trajectories (MATs). Common examples of aspects can be points of interest, user photos, transportation means, weather conditions, social media posts, and many more. However, the literature does not currently provide a consensus on how to semantically enrich mobility data with aspects, particularly in dynamic scenarios where semantic information is extracted from numerous and heterogeneous external data sources. In this work, we aim to address this issue by presenting a comprehensive methodology to facilitate end users in instantiating their semantic enrichment processes of movement data. The methodology is agnostic to semantic aspects and external semantic data sources. The vision behind our methodology rests on three pillars: (1) three design principles which we argue are necessary for designing systems capable of instantiating arbitrary semantic enrichment processes; (2) the MAT-Builder system, which embodies these principles; (3) the use of an RDF knowledge graph-based representation to store MATs datasets, thereby enabling uniform querying and analysis of enriched movement data. We qualitatively evaluate the methodology in two complementary example scenarios, where we show both the potential in generating interesting and useful semantically enriched mobility datasets, and the expressive power in querying the resulting RDF trajectories with SPARQL.Source: IEEE ACCESS, vol. 11, pp. 90857-90875
DOI: 10.1109/access.2023.3307824
Project(s): MobiDataLab
, MASTER , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
Predicting EV parking behaviour in shared premises
Monteiro De Lira V, Pallonetto F, Gabrielli L, Renso C
The global electric car sales continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises. The final objective is estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. We test the proposed approach in a combination of datasets from 2 different campus facilities in Italy and Brazil. The overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: CEUR WORKSHOP PROCEEDINGS. Ioannina, Greece, 28/03/2023
Project(s): ERANet SmartGridPlus
Monteiro De Lira V, Pallonetto F, Gabrielli L, Renso C
The global electric car sales continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises. The final objective is estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. We test the proposed approach in a combination of datasets from 2 different campus facilities in Italy and Brazil. The overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: CEUR WORKSHOP PROCEEDINGS. Ioannina, Greece, 28/03/2023
Project(s): ERANet SmartGridPlus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Contribution to book
Open Access
Message from the PC and General co-Chairs SSTD 2023
Baihua Z, Mokbel M, Nascimento Ma, Renso C, Zeitouni K Züfle A
Preface of the conference SSTD 2023.
Baihua Z, Mokbel M, Nascimento Ma, Renso C, Zeitouni K Züfle A
Preface of the conference SSTD 2023.
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
MAT-CA: a tool for Multiple Aspect Trajectory Clustering Analysis
Santos Y, Giuliani R, Portela T, Renso C, Carvalho J
Multiple aspect trajectory (MAT) is a relevant concept that enables mining interesting patterns moving objects for di!erent applications. This new way of looking at trajectories includes a semantic dimension, which presents the notion of aspects that are relevant facts of the real world that add more meaning to spatio-temporal data. The high dimensionality and heterogeneity of these data makes clustering a very challenging task both in terms of e"ciency and quality. The present demo o!ers a tool, called MAT-CA, to support the user in the clustering task of MATs, speci#cally for identifying and visualizing the hidden patterns. The MAT-CA join into the same tool a multiple aspects trajectories clustering method and visual analysis of the results. We illustrate the use of the tool for o!ering both clustering output visualization and statistics.DOI: 10.1145/3615885.3628009
Project(s): MASTER
Metrics:
Santos Y, Giuliani R, Portela T, Renso C, Carvalho J
Multiple aspect trajectory (MAT) is a relevant concept that enables mining interesting patterns moving objects for di!erent applications. This new way of looking at trajectories includes a semantic dimension, which presents the notion of aspects that are relevant facts of the real world that add more meaning to spatio-temporal data. The high dimensionality and heterogeneity of these data makes clustering a very challenging task both in terms of e"ciency and quality. The present demo o!ers a tool, called MAT-CA, to support the user in the clustering task of MATs, speci#cally for identifying and visualizing the hidden patterns. The MAT-CA join into the same tool a multiple aspects trajectories clustering method and visual analysis of the results. We illustrate the use of the tool for o!ering both clustering output visualization and statistics.DOI: 10.1145/3615885.3628009
Project(s): MASTER
Metrics:
2023
Conference article
Open Access
A data augmentation algorithm for trajectory data
Haranwala Yj, Spadon G, Renso C, Soares A
The growing prevalence of location-based devices has resulted in a signi!cant abundance of location data from various tracking vendors. Nevertheless, a noticeable de!cit exists regarding readily accessible, extensive, and publicly available datasets for research purposes, primarily due to privacy concerns and ownership constraints. There is a pressing need for expansive datasets to advance machine learning techniques in this domain. The absence of such resources currently represents a substantial hindrance to research progress in this !eld. Data augmentation is emerging as a popular technique to mitigate this issue in several domains. However, applying state-of-the-art techniques as-is proves challenging when dealing with trajectory data due to the intricate spatio-temporal dependencies inherent to such data. In this work, we propose a novel strategy for augmenting trajectory data that applies a geographical perturbation on trajectory points along a trajectory. Such a perturbation results in controlled changes in the raw trajectory and, consequently, causes changes in the trajectory feature space. We test our strategy in two trajectory datasets and show a performance improvement of approximately 20% when contrasted with the baseline. We believe this strategy will pave the way for a more comprehensive framework for trajectory data augmentation that can be used in !elds where few labeled trajectory data are available for training machine learning models.DOI: 10.1145/3615885.3628008
Project(s): MASTER
Metrics:
Haranwala Yj, Spadon G, Renso C, Soares A
The growing prevalence of location-based devices has resulted in a signi!cant abundance of location data from various tracking vendors. Nevertheless, a noticeable de!cit exists regarding readily accessible, extensive, and publicly available datasets for research purposes, primarily due to privacy concerns and ownership constraints. There is a pressing need for expansive datasets to advance machine learning techniques in this domain. The absence of such resources currently represents a substantial hindrance to research progress in this !eld. Data augmentation is emerging as a popular technique to mitigate this issue in several domains. However, applying state-of-the-art techniques as-is proves challenging when dealing with trajectory data due to the intricate spatio-temporal dependencies inherent to such data. In this work, we propose a novel strategy for augmenting trajectory data that applies a geographical perturbation on trajectory points along a trajectory. Such a perturbation results in controlled changes in the raw trajectory and, consequently, causes changes in the trajectory feature space. We test our strategy in two trajectory datasets and show a performance improvement of approximately 20% when contrasted with the baseline. We believe this strategy will pave the way for a more comprehensive framework for trajectory data augmentation that can be used in !elds where few labeled trajectory data are available for training machine learning models.DOI: 10.1145/3615885.3628008
Project(s): MASTER
Metrics:
2023
Conference article
Open Access
Summarizing trajectories using semantically enriched geographical context
Pugliese C, Lettich F, Pinelli F, Renso C
The proliferation of tracking sensors in today's devices has led to the generation of high-frequency, high-volume streams of mobility data capturing the movements of various objects. These movement data can be enriched with semantic contextual information, such as activities, events, user preferences, and more, generating semantically enriched trajectories. Creating and managing these types of trajectories presents challenges due to the massive data volume and the heterogeneous, complex semantic dimensions. To address these issues, we introduce a novel approach, MAT-Sum, which uses a location-centric enrichment perspective to summarize massive volumes of mobility data while preserving essential semantic information. Our approach enriches geographical areas with semantic aspects to provide the underlying context for trajectories, enabling effective data reduction through trajectory summarization. In the experimental evaluation, we show that MAT-Sum effectively minimizes trajectory volume while retaining a good level of semantic quality, thus presenting a viable solution to the relevant issue of managing massive mobility data.DOI: 10.1145/3589132.3625587
Project(s): MobiDataLab, MASTER , SoBigData-PlusPlus
Metrics:
Pugliese C, Lettich F, Pinelli F, Renso C
The proliferation of tracking sensors in today's devices has led to the generation of high-frequency, high-volume streams of mobility data capturing the movements of various objects. These movement data can be enriched with semantic contextual information, such as activities, events, user preferences, and more, generating semantically enriched trajectories. Creating and managing these types of trajectories presents challenges due to the massive data volume and the heterogeneous, complex semantic dimensions. To address these issues, we introduce a novel approach, MAT-Sum, which uses a location-centric enrichment perspective to summarize massive volumes of mobility data while preserving essential semantic information. Our approach enriches geographical areas with semantic aspects to provide the underlying context for trajectories, enabling effective data reduction through trajectory summarization. In the experimental evaluation, we show that MAT-Sum effectively minimizes trajectory volume while retaining a good level of semantic quality, thus presenting a viable solution to the relevant issue of managing massive mobility data.DOI: 10.1145/3589132.3625587
Project(s): MobiDataLab
, MASTER , SoBigData-PlusPlus Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS
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.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.DOI: 10.1145/3589132.3625623
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
Towards a representativeness measure for summarized trajectories with multiple aspects
Lago Machado V, Tortelli Portela T, Renso C, Dos Santos Mello R
Large trajectory datasets have led to the development of summarization methods. However, evaluating the efficacy of these techniques can be complex due to the lack of a suitable representativeness measure. In the context of multi-aspect trajectories, current summarization lacks evaluation methods. To address this, we introduce RMMAT, a novel representativeness measure that combines similarity metrics and covered information to offer adaptability to diverse data and analysis needs. Our innovation simplifies summarization technique evaluation and enables deeper insights from extensive trajectory data. Our evaluation of real-world trajectory data demonstrates RMMAT as a robust Representativeness Measure for Summarized Trajectories with Multiple Aspects.Project(s): MASTER, SoBigData-PlusPlus
Lago Machado V, Tortelli Portela T, Renso C, Dos Santos Mello R
Large trajectory datasets have led to the development of summarization methods. However, evaluating the efficacy of these techniques can be complex due to the lack of a suitable representativeness measure. In the context of multi-aspect trajectories, current summarization lacks evaluation methods. To address this, we introduce RMMAT, a novel representativeness measure that combines similarity metrics and covered information to offer adaptability to diverse data and analysis needs. Our innovation simplifies summarization technique evaluation and enables deeper insights from extensive trajectory data. Our evaluation of real-world trajectory data demonstrates RMMAT as a robust Representativeness Measure for Summarized Trajectories with Multiple Aspects.Project(s): MASTER
, SoBigData-PlusPlus
See at:
CNR IRIS | ISTI Repository | www.geoinfo.info | CNR IRIS
2023
Journal article
Open Access
Making it easy for transport stakeholders to share mobility data
Chevallier T., Lauer J., Renso C., Blanco-Justicia A., De Ryck D., Papacharalampous A.
With the emergence of new mobility services, an increasing amount of data is being produced. However, while it is recognized that data sharing can open up new opportunities and lead to more efficient processes and new products, there is still a lot of reluctance to share data. The EU-funded MobiDataLab project works to remove these limitations and to foster the sharing of data amongst transport authorities, operators and other mobility stakeholders. According to the FAIR principles (findable, accessible, interoperable and reusable), the MobiDataLab project provides a “transport cloud”, that is an infrastructure to build new solutions with mobility data and services. With a close contribution between a reference group, the project team and contributors of virtual and living labs, the project will identify current challenges and work with the relevant interest groups on solutions.Source: TRANSPORTATION RESEARCH PROCEDIA, vol. 72, pp. 2237-2244
DOI: 10.1016/j.trpro.2023.11.711
Project(s): MobiDataLab
Metrics:
Chevallier T., Lauer J., Renso C., Blanco-Justicia A., De Ryck D., Papacharalampous A.
With the emergence of new mobility services, an increasing amount of data is being produced. However, while it is recognized that data sharing can open up new opportunities and lead to more efficient processes and new products, there is still a lot of reluctance to share data. The EU-funded MobiDataLab project works to remove these limitations and to foster the sharing of data amongst transport authorities, operators and other mobility stakeholders. According to the FAIR principles (findable, accessible, interoperable and reusable), the MobiDataLab project provides a “transport cloud”, that is an infrastructure to build new solutions with mobility data and services. With a close contribution between a reference group, the project team and contributors of virtual and living labs, the project will identify current challenges and work with the relevant interest groups on solutions.Source: TRANSPORTATION RESEARCH PROCEDIA, vol. 72, pp. 2237-2244
DOI: 10.1016/j.trpro.2023.11.711
Project(s): MobiDataLab
Metrics:
See at:
Transportation Research Procedia | CNR IRIS | www.sciencedirect.com | CNR IRIS
2022
Journal article
Open Access
MAT-Index: an index for fast multiple aspect trajectory similarity measuring
De Souza Apr, Renso C, Perego R, Bogorny V
The semantic enrichment of mobility data with several information sources has led to a new type of movement data, the so-called multiple aspect trajectories. Comparing multiple aspect trajectories is crucial for several analysis tasks such as querying, clustering, similarity, and classification. Multiple aspect trajectory similarity measurement is more complex and computationally expensive, because of the large number and heterogeneous aspects of space, time, and semantics that require a different treatment. Only a few works in the literature focus on optimizing all these dimensions in a single solution, and, to the best of our knowledge, none of them proposes a fast point-to-point comparison. In this article we propose the Multiple Aspect Trajectory Index, an index data structure for optimizing the point-to-point comparison of multiple aspect trajectories, considering its three basic dimensions of space, time, and semantics. Quantitative and qualitative evaluations show a processing time reduction of up to 98.1%.Source: TRANSACTIONS IN GIS (PRINT)
DOI: 10.1111/tgis.12889
Project(s): MASTER
Metrics:
De Souza Apr, Renso C, Perego R, Bogorny V
The semantic enrichment of mobility data with several information sources has led to a new type of movement data, the so-called multiple aspect trajectories. Comparing multiple aspect trajectories is crucial for several analysis tasks such as querying, clustering, similarity, and classification. Multiple aspect trajectory similarity measurement is more complex and computationally expensive, because of the large number and heterogeneous aspects of space, time, and semantics that require a different treatment. Only a few works in the literature focus on optimizing all these dimensions in a single solution, and, to the best of our knowledge, none of them proposes a fast point-to-point comparison. In this article we propose the Multiple Aspect Trajectory Index, an index data structure for optimizing the point-to-point comparison of multiple aspect trajectories, considering its three basic dimensions of space, time, and semantics. Quantitative and qualitative evaluations show a processing time reduction of up to 98.1%.Source: TRANSACTIONS IN GIS (PRINT)
DOI: 10.1111/tgis.12889
Project(s): MASTER
Metrics:
See at:
CNR IRIS | onlinelibrary.wiley.com | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
A federated cloud solution for transnational mobility data sharing
Carlini E, Chevalier T, Dazzi P, Lettich F, Perego R, Renso C, Trani S
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: CEUR WORKSHOP PROCEEDINGS, pp. 586-592. Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION, MobiDataLab
Carlini E, Chevalier T, Dazzi P, Lettich F, Perego R, Renso C, Trani S
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: CEUR WORKSHOP PROCEEDINGS, pp. 586-592. Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION
, MobiDataLab
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
Predicting vehicles parking behaviour for EV recharge optimization
Monteiro De Lira V, Pallonetto F, Gabrielli L, Renso C
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: CEUR WORKSHOP PROCEEDINGS, pp. 199-206. Tirrenia, Pisa, Italy, 19-22/06/2022
Monteiro De Lira V, Pallonetto F, Gabrielli L, Renso C
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: CEUR WORKSHOP PROCEEDINGS, pp. 199-206. Tirrenia, Pisa, Italy, 19-22/06/2022
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
MAT-Builder: a system to build semantically enriched trajectories
Pugliese C, Lettich F, Renso C, Pinelli F
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect trajectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-Builder, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-Builder has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities. The running example provided towards the end of the paper highlights how MAT-Builder's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.Source: CEUR WORKSHOP PROCEEDINGS, pp. 175-182. Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MobiDataLab, MASTER
Pugliese C, Lettich F, Renso C, Pinelli F
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect trajectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-Builder, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-Builder has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities. The running example provided towards the end of the paper highlights how MAT-Builder's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.Source: CEUR WORKSHOP PROCEEDINGS, pp. 175-182. Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MobiDataLab
, MASTER
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS