2025 Conference article Open Access

Multi-Sensor Inferred Trajectories (MUSIT) for vessel mobility
Ray C., Troupiotis-Kapeliaris A., Kontopoulos I., Andronikou V., Nasios I., Piliouras N., Chevallier T., Delmas V., Tserpes K., Zissis D., Renso C., Carlini E.
The abundance of tracking sensors in recent years has led to the generation of high-frequency and high-volume streams of data, including vessel locations, marine observations captured from many sensors (living resources, sea state, weather conditions, etc.). However, there are cases where the trajectory of a moving object has gaps, errors, or is unavailable. Thus, while a vast pool of tracking data is available, these data remain unexplored or underutilized and have the potential to reveal important information. The MUlti-Sensor Inferred Trajectories (MUSIT) project aims to explore and fuse data from all heterogeneous sources to provide detailed information about the location and behavior of a moving object, reduce gaps, and produce a refined and inferred trajectory with minimal errors. The fusion of multi-sensor data is required to fill in the trajectory gaps of moving objects and attach useful semantics to the trajectory. Artificial intelligence algorithms and spatiotem-poral methodologies that can fuse information and infer missing knowledge are also crucial. Furthermore, different representation models from multiple sensors will also be explored. Multi-sensor datasets will be designed and made available to experiment with models, fusion and trajectory inference algorithms, and deduce new knowledge. Therefore, the MUSIT project will tackle these issues in a three-step process: i) data collection and creation, ii) exploitation and utilization of cross-domain representation models for trajectories, and iii) analysis and processing of outcomes to produce information-rich results related to vessel monitoringDOI: 10.1109/oceans58557.2025.11104731
Project(s): MUSIT
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2025 Other Open Access

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2025 Contribution to book Open Access

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2025 Conference article Open Access

Urban region embeddings from service-specific mobile traffic data
Loddi G., Pugliese C., Lettich F., Pinelli F., Renso C.
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”
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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”
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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
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2024 Journal article Open Access

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2024 Contribution to book Open Access

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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
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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
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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

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2023 Contribution to book Open Access

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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
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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
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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
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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
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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

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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
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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
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