We present a demo of RAGTrip, a modular conversational system that integrates Large Language Models (LLMs), spatial reasoning, and information retrieval to generate personalized walking itineraries in urban environments. Unlike traditional route planners or closed-book LLMs, RAGTrip interprets nuanced user preferences, avoids hallucinations, and grounds its suggestions in real-world geographic and factual data. The system features an interactive conversational interface that engages users in refining both the itinerary and the attractions to visit. Through dynamic map visualizations and contextual responses, users can explore and iteratively customize their routes. The demo includes a toggle to enable or disable Retrieval-Augmented Generation (RAG), allowing direct comparison between RAG-enhanced and closed-book LLM responses. This highlights the value of combining spatial and semantic grounding in conversational itinerary recommendation.

With the advent of modern$4 \mathrm{G} / 5 \mathrm{G}$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.

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.

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.

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.

With the rapid increasing availability of information and popularization of mobility devices, trajectories have become more complex in their form. Trajectory data is now high dimensional, and often associated with heterogeneous sources of semantic data, that are called Multiple Aspect Trajectories. The high dimensionality and heterogeneity of these data makes classification a very challenging task both in term of accuracy and in terms of efficiency. The present demo offers a tool, called AUTOMATISE, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. The AUTOMATISE integrates into a unique platform the fragmented approaches available in the literature for multiple aspects trajectories and, in general, for multidimensional sequence classification into a unique web-based and python library system. We illustrate the architecture and the use of the tool for offering both movelets visualization and a complete configuration of classification experimental settings.

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 tra-jectories, 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 to the system and set up their own semantic enrichment process. The demonstration scenario, which will be showcased during the demo session, highlights how MAT-BUILDER's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.

A plethora of applications and devices reporting their locations generate massive amounts of spatiotemporal data along with other useful information. These data can form trajectories with sequences...

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.

Most of the trajectory datasets only record the spatio-temporal position of the moving object, thus lacking semantics and this is due to the fact that this information mainly depends on the domain expert labeling, a time-consuming and complex process. This paper is a contribution in facilitating and supporting the manual annotation of trajectory data thanks to a visual-analytics-based platform named VISTA. VISTA is designed to assist the user in the trajectory annotation process in a multi-role user environment. A session manager creates a tagging session selecting the trajectory data and the semantic contextual information. The VISTA platform also supports the creation of several features that will assist the tagging users in identifying the trajectory segments that will be annotated. A distinctive feature of VISTA is the visual analytics functionalities that support the users in exploring and processing the trajectory data, the associated features and the semantic information for a proper comprehension of how to properly label trajectories.

A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajectories into homogeneous segments based on some criteria. Although trajectory segmentation has been the object of several approaches in the last decade, a proposal based on a semi-supervised approach remains inexistent. A semi-supervised approach means that a user labels manually a small set of trajectories with meaningful segments and, from this set, the method infers in an unsupervised way the segments of the remaining trajectories. The main advantage of this method compared to pure supervised ones is that it reduces the human effort to label the number of trajectories. In this work, we propose the use of the Minimum Description Length (MDL) principle to measure homogeneity inside segments. We also introduce the Reactive Greedy Randomized Adaptive Search Procedure for semantic Semi-supervised Trajectory Segmentation (RGRASP-SemTS) algorithm that segments trajectories by combining a limited user labeling phase with a low number of input parameters and no predefined segmenting criteria. The approach and the algorithm are presented in detail throughout the paper, and the experiments are carried out on two real-world datasets. The evaluation tests prove how our approach outperforms state-of-the-art competitors when compared to ground truth.

Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal periods: before, during and after an event. The categories of features used to train the classifier reflect four different dimensions of social media: textual, temporal, social, and multimedia content. We detail the approach followed to design the feature space and report on experiments conducted on two large music festivals in the UK, namely the VFestival and Creamfields events. Our attendance classifier attains very high accuracy with the highest result observed for the Creamfields dataset ~87% accuracy to classify users that will participate in the event.

An increasing number of people share their thoughts and the images of their lives on social media platforms. People are exposed to food in their everyday lives and share on-line what they are eating by means of photos taken to their dishes. The hashtag #foodporn is constantly among the popular hashtags in Twitter and food photos are the second most popular subject in Instagram after selfies. The system that we propose, WorldFoodMap, captures the stream of food photos from social media and, thanks to a CNN food image classifier, identifies the categories of food that people are sharing. By collecting food images from the Twitter stream and associating food category and location to them, WorldFoodMap permits to investigate and interactively visualize the popularity and trends of the shared food all over the world.

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.

Recent research has pointed out the needs and advantages of the semantic enrichment of movement data, a process where trajectories are partitioned into homogeneous segments that are annotated with contextual information. However, the lack of a comprehensive and well-defined framework for the enrichment makes this process difficult and error-prone. In this paper, we therefore propose a conceptual framework for the semantic enrichment of movement data, which benefits from the emerging Web of Data (or Linked Open Data) both as a unifying formalism and as the source of contextual data, which can be greatly useful for trajectories enrichment. Moreover, the semantic structure of such sources makes it easier to share and process enriched trajectories. We illustrate the enrichment process by presenting a case study in the tourism domain.

Several among our daily activities, like traveling to a tourist attraction, are better enjoyed with a group of friends. However, finding the best travel companions is sometimes tricky since we need to form a group of people combining the interest in the proposed destination with the friendship relations among the group members. In this paper we cope with this problem by proposing a new method to recommend the best group of friends with whom to enjoy a specific item, i.e., a travel destination or a venue to visit. Our approach provides a new and original perspective on recommendation: given a user, her social network and a recommended item that is relevant for the user, we want to suggest the best group of friends with whom enjoying the item. This approach differs from traditional group recommendation since it tries to maximize two orthogonal aspects: i) the relevance of the recommended item for every member of the group, and ii), the intra-group social relationships. We introduce the Group Finder framework defining the User-Item Group Formation problem and the possible solutions. We assess our approach in the domain of location recommendation and experiment the proposed solutions using four different publicly available Location Based Social Network (LBSN) datasets. The results achieved confirm the effectiveness and the feasibility of the proposed solutions that outperform strong baselines.

ComeWithMe is an activity oriented carpooling service that enlarges the candidate destinations of a ride request by considering alternative places where the desired activity can be performed. It is based on the observation that individuals often move towards a place to perform an activity while the activity is often not strictly associated with a single place, as one may go for shopping or eating to many different locations. Activity-oriented carpooling hugely increases the number of rides matching a query, thus introducing requirements on system responsiveness and ranking effectiveness that are not common to traditional carpooling services. The demoed system implements the ComeWithMe service in almost its entirety, and includes the back-end and a user-friendly mobile application for smart-phones aimed at achieving users' acceptance and usability.

The increasing availability of positioning data fostered a number of new applications where the knowledge about mobility patterns is essential. However, the research conducted so far on mobility analysis focused on the geometric aspect at the expenses of the semantics of the movement. In this paper, we offer a new vision of semantic trajectory data warehouse that combines the pure geometrical features in terms of temporal and geographical coordinates with more semantic-related contextual information such as the goal of the movement and the transportation means of the moving object, or the activity performed by the moving entity. The conceptual model proposed is called SWOT and is capable of combining these aspects, thus providing a considerable improvement in answering semantic enriched mobility queries.

In recent years, the exponential growth of positioning-enabled devices have allowed us to study the mobility behavior of individuals analyzing their collected tracks. In this context, a small, but steadily increasing part of the literature is looking at the semantic aspects of mobility. This paper presents a contribution to this trend, and is concerned with the definition of semantic regularity profiles. We based our methodology on the entropy of both spatial and temporal frequency of visits of individuals to places to perform an activity. This allows us to define the concept of semantic regular or irregular user behavior identifying users who are more or less loyal to the same places in contrast to the flexibility in visiting different places to perform an activity. We experiment on a crowdsensed trajectory dataset annotated by the visited Points of Interest which represent the activity performed. Analysis evidence that the regularity depends on the particular activity to be performed.

This information about our GSM calls is stored by the TelCo operator in large volumes and with strict privacy constraints making it challenging the analysis of these fingerprints for inferring mobility behavior. This paper proposes a strategy for mobility behavior identification based on aggregated calling profiles of mobile phone users. This compact representation of the user call profiles is the input of the mining algorithm for automatically classifying various kinds of mobility behavior. A further advantage of having defined the call profiles is that the analysis phase is based on summarized privacy-preserving representation of the original data. We show how these call profiles permit to design a two step process - implemented into a system - based on a bootstrap phase and a running phase for classifying users into behavior categories. We evaluated the system in two case studies where individuals are classified into residents, commuters and visitors. We conclude the paper with a discussion which emphasizes the role of the call profiles for the design of a new collaboration model between data provider and data analyst.

In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.

Positioning systems, combined with inexpensive communication technologies, open interesting possibilities to implement real-time applications that monitor vehicles and support decision making. This paper first discusses basic requirements for proactive real-time monitoring applications. Then, it describes how to structure and geo-reference unstructured text information available on the Internet, with a focus on road conditions change and using available geocoding services. Lastly, the paper outlines an application that monitors a fleet of trucks and incorporates proactive features.

We analyze urban mobility and public places under a new perspective: how can we feature the places in a city based on how people move among them? To answer this question we need to combine places, like points of interest, with mobility information like the trajectories of individuals moving within a city. To accomplish this, we propose a methodology based on complex network analysis: we build a network of points of interests by connecting places by the individual trajectories passing through them. From such network we compute communities finding groups places highly connected by the mobility of the individuals. We present a case study on real trajectory dataset on the city of Milan, showing a complementary view on the urban mobility that is not covered by the state-of-the art techniques on mobility analysis.

A massive amount of data on moving object trajectories is available today. However, it is still a major challenge to process such information in order to explain moving object interactions, which could help in revealing non-trivial behavioral patterns. To that end, we consider a complex networks-based representation of trajectory data. Frequent encounters among moving objects (trajectory encounters) are used to create the network edges whereas nodes represent trajectories. A real trajectory dataset of vehicles moving within the City of Milan allows us to study the structure of vehicle interactions and validate our method. We create seven networks and compute the clustering coefficient, and the average shortest path length comparing them with those of the Erdős-Rényi model. Our analysis shows that all computed trajectory networks have the small world effect and the scale-free feature similar to the internet and biological networks. Finally, we discuss how these results could be interpreted in the light of the traffic application domain.

Tracking technologies are able to provide high-resolution movement data that can advance research in different fields, such as tourism management. In this specific field, developing methods to extract moving flock patterns from such data are particularly relevant to enable us to improve our knowledge of the nature of recreational use interactions, which is crucial for a good management of attractions and for designing sustainable development policies. However, ‘flocking’ has been usually associated with the form of collective movement of a large group of birds, fish, insects and certain mammals as well. Very few research efforts have been devoted in finding flock patterns associated with pedestrian movement. In this work, we propose a moving flock pattern definition and a corresponding extraction algorithm based on the notion of collective coherence. We use the term collective coherence to refer to the spatial closeness over some time duration with a minimum number of members. Furthermore, we evaluate the proposed algorithm by applying it to two different pedestrian movement datasets, which have been gathered from visitors of two recreational parks. The results show that the algorithm is capable of extracting moving flock patterns, disqualifying the patterns with flock members that remain stationary in a common place during the considered time interval.

The growing availability of mobile devices produces an enor- mous quantity of personal tracks which calls for advanced analysis methods capable of extracting knowledge out of massive trajectories datasets. In this paper we present an experiment on a real world scenario that demonstrates the strong analytical power of massive, raw trajectory data made available as a by-product of telecom services, in unveiling the complexity of urban mobility. The experiment has been made possible by the GeoPKDD system, an integrated plat- form for complex analysis of mobility data. The system com- bines spatio-temporal querying capabilities with data min- ing and semantic technologies, thus providing a full support for the Mobility Knowledge Discovery process.

In this work we propose DAEDALUS, a formal framework and system, specifically focussed on progressive combination of mining and querying operators. The core component of DAEDALUS is the MO-DMQL query language that extends SQL in two respects, namely a pattern definition operator and the capability to uniform manipulating both raw data and unveiled patterns. DAEDALUS system is specifically focussed on movement data and has been implemented as a query execution layer on top of the Hermes Moving Object Database. The expressiveness and usefulness of the MODMQL language as well as the computational capabilities of DAEDALUS are qualitatively evaluated by means of a case study.

We propose a formalism and a programming environment in which sophisticated spatio-temporal reasoning can be performed, while keeping the capabilities of manipulating and presenting large amounts of geographical data, typical of commercial Geographical Information Systems (GISs). The spatio-temporal knowledge representation language, named MuTACLP+, is based on constraint logic programming and is integrated via a middleware of commands and translation features with a commercial GIS. The paper presents the language, the architecture of the environment, and a few examples of its use in the field of event planning.