Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies.In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them.To accomplish this task, we adopt a bottom-up network construction approach that combines three years' worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports.Through this representation, we utilize machine learning to assess the relative significance of various port features.Our model examined such features and revealed that geographical characteristics and the port's depth are indicators of a port's importance to the Ports Network.Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to the extent of ports.Our work aims to inform decision-making processes related to port development, resource allocation, and infrastructure planning within the industry.

Named Entity Recognition (NER) is a challenging learning task of identifying and classifying entity mentions in texts into predefined categories. In recent years, deep learning (DL) methods empowered by distributed representations, such as word- and character-level embeddings, have been employed in NER systems. However, for information extraction in Police narrative reports, the performance of a DL-based NER approach is limited due to the presence of fine-grained ambiguous entities. For example, given the narrative report “Anna stole Ada’s car”, imagine that we intend to identify the VICTIM and the ROBBER, two sub-labels of PERSON. Traditional NER systems have limited performance in categorizing entity labels arranged in a hierarchical structure. Furthermore, it is unfeasible to obtain information from knowledge bases to give a disambiguated meaning between the entity mentions and the actual labels. This information must be extracted directly from the context dependencies. In this paper, we deal with the Hierarchical Entity-Label Disambiguation problem in Police reports without the use of knowledge bases. To tackle such a problem, we present HELD, an ensemble model that combines two components for NER: a BLSTM-CRF architecture and a NER tool. Experiments conducted on a real Police reports dataset show that HELD significantly outperforms baseline approaches.

In this article, we consider the External Sensor Trajectory Prediction problem for stolen vehicle trajectories. This analysis brings new challenges to the problem, as crime patterns are dynamic and drivers of stolen vehicles tend to move away from the sensors, which increases data dispersion. We analyze the effectiveness of different machine learning models and propose semantic enrichment with criminal data and points of interest to solve our problem. We also investigate the best attributes to improve EST prediction models, and how different spatial level representations can leverage prediction accuracy.

Transitive Halifax is an activity-oriented mobility service that allows users to search for bus routes toward places where they can perform their desired activities. The service is based on the observation that individuals often go to a place to conduct an activity. Simultaneously, the activity is often not strictly related to a single place since one may go shopping or eating in different locations. Transitive Halifax has a web interface that helps the user find the most relevant bus routes and bus stops candidates that they could use to go to places where they can perform their intended activity. The system implements a search engine that ranks the bus stops candidates according to the user's preferences and desired activities.

People living in highly-populated cities increasingly suffer an impoverishment of their quality of life due to pollution and traffic congestion problems caused by the huge number of circulating vehicles. The focus of this thesis is on improving ride sharing systems as a possible solution to reduce the number of circulating vehicles.

To assure a timely fruition of media and interactive applications to end users is a complex challenge, especially when potentially spread worldwide, at home or in mobility. It in fact requires a careful placement of the software services on the right computational resources, such that those services are placed as close as possible to end users to mitigate the effect of network on the user experience. In this demo paper, we present a tool that aims to facilitate the placement of latency sensitive applications on computational resources, by considering the geographical positioning of the user demand, the user experience, and the budget limitation of application owners.

People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can be performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the top-k most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can be saved and how the ranking function can be tuned to enforce pro-environment behaviors.

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.

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.

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.