Gianni Barlacchi

Università degli Studi di Trento | UNITN · Department of Information Engineering and Computer Science

Next-location prediction, consisting of forecasting a user’s location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading. Several predictors have been proposed in the last few years to address it, including last-generation ones based on deep learning. This...

Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap – up to a few years – between the data collection process and the computation and publication of relevant statistics. This gap is a significant draw...

The last decade has witnessed the emergence of massive mobility datasets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These datasets have fostered a vast scientific production on various applications of mobility analysis, ranging from computational epidemiology to urban planning an...

Next-location prediction, consisting of forecasting a user's location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading. Several predictors have been proposed in the last few years to address it, including last-generation ones based on deep learning. This...

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the ap...

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose...

Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap - up to a few years - between the data collection process and the computation and publication of relevant statistics. This gap is a significant draw...

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the ap...

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propo...

In this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures in kernel machines, e.g., Support Vector Machines, using a new Tree Kernel, i.e., the Hierarchical...

The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These data sets have fostered a vast scientific production on various applications of human mobility analysis, ranging from computational epidemiology to urban pla...

The availability of geo-spatial mobility data (e.g., GPS traces, call detail records, social media records) is a trend that will grow in the near future. In particular, this will happen when the shift from traditional vehicles to autonomous, self-driving, vehicles, will transform our society, the economy and the environment. For this reason, unders...

In this paper, we study how to model taxi drivers' behavior and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well-studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of ele...

The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main...

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g. floods terroristic attacks, etc...). In this perspective, we propose ACHILLES, an application to mod...

The rapid inclusion of tracking technologies in our personal devices opened the doors to the analysis and visualization of large sets of geo-spatial mobility data, in particular GPS traces. In this tutorial we will present a concise and intuitive overview on both fundamental modeling principles of human mobility, and machine learning models applica...

In this paper, we study how to model taxi drivers’ behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of el...

Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the mobility behavior of a person can also reveal relevant information about her/his health conditions. In this p...

ThŒe availability of massive data describing human mobility o‚ffers the possibility to design simulation tools to control and improve transportation systems. In this perspective, we propose a visual and data-driven simulation tool based on a multiplex network representation of mobility data, where every layer describes people's movements with a giv...

The availability of massive data describing human mobility offers the possibility to design simulation tools to control and improve transportation systems. In this perspective, we propose a visual and data-driven simulation tool based on a multiplex network representation of mobility data, where every layer describes people’s movements with a given...

The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geogra...

In this paper, we study methods for improving the quality of automatic extraction of answer candidates for automatic resolution of crossword puzzles (CPs), which we set as a new IR task. Since automatic systems use databases containing previously solved CPs, we define a new effective approach consisting in querying the database (DB) with a search e...

Automatic resolution of Crossword Puz-zles (CPs) heavily depends on the qual-ity of the answer candidate lists produced by a retrieval system for each clue of the puzzle grid. Previous work has shown that such lists can be generated using In-formation Retrieval (IR) search algorithms applied to the databases containing previ-ously solved CPs and re...

In this paper, we study the impact of relational and syntactic representations for an interesting and challenging task: the automatic resolution of crossword puzzles. Automatic solvers are typically based on two answer retrieval modules: (i) a web search engine, e.g., Google, Bing, etc. and (ii) a database (DB) system for accessing previously resol...

We present ERNESTA (Enhanced Readability through a Novel Event-based Simplification Tool), the first sentence simplification system for Italian, specifically developed to improve the comprehension of factual events in stories for children with low reading skills. The system performs two basic actions: First, it analyzes a text by resolving anaphora...