SLIM : Scalable Linkage of Mobility Data

We present a scalable solution to link entities across mobility datasets using their spatio-temporal information. This is a fundamental problem in many applications such as linking user identities for security, understanding privacy limitations of location based services, or producing a unified dataset from multiple sources for urban planning. Such integrated datasets are also essential for service providers to optimise their services and improve business intelligence. In this paper, we first propose a mobility based representation and similarity computation for entities. An efficient matching process is then developed to identify the final linked pairs, with an automated mechanism to decide when to stop the linkage. We scale the process with a locality-sensitive hashing (LSH) based approach that significantly reduces candidate pairs for matching. To realize the effectiveness and efficiency of our techniques in practice, we introduce an algorithm called SLIM. In the experimental evaluation, SLIM outperforms the two existing state-of-the-art approaches in terms of precision and recall. Moreover, the LSH-based approach brings two to four orders of magnitude speedup

Mobile networks and internet of things infrastructures to characterize smart human mobility

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.This work has been supported by FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has also been supported by national funds through FCT–Fundação para a Ciência e Tecnologia through project UIDB/04728/202

HUMAN INTERACTIONS IN PHYSICAL AND VIRTUAL SPACES: A GIS-BASED TIME-GEOGRAPHIC EXPLORATORY APPROACH

Information and communication technologies (ICT) such as cell phone and the Internet have extended opportunities of human activities and interactions from physical spaces to virtual spaces. The relaxed spatio-temporal constraints on individual activities may affect human activity-travel patterns, social networks, and many other aspects of society. A challenge for research of human activities in the ICT age is to develop analytical environments that can help visualize and explore individual activities in virtual spaces and their mutual impacts with physical activities. This dissertation focuses on extending the time-geographic framework and developing a spatio-temporal exploratory environment in a space-time geographic information system (GIS) to facilitate research of human interactions in both physical and virtual spaces. In particular, this dissertation study addresses three research questions. First, it extends the time-geographic framework to assess the impacts of phone usage on potential face-to-face (F2F) meeting opportunities, as well as dynamic changes in potential F2F meeting opportunities over time. Secondly, this study extends the time-geographic framework to conceptualize and represent individual trajectories in an online social network space and to explore potential interaction opportunities among people in a virtual space. Thirdly, this study presents a spatio-temporal environment in a space-time GIS to facilitate exploration of the relationships between changes in physical proximity and changes in social closeness in a virtual space. The major contributions of this dissertation include: (1) advancing the time-geographic framework in its ability of exploring processes of virtual communication alerting physical activity opportunities; (2) extending some concepts of the classical time geography from a physical space to a virtual space for representing and exploring virtual interaction patterns; (3) developing a space-time GIS that is useful for exploring patterns of individual activities and interactions in both physical and virtual spaces, as well as the interactions between these two spaces

Analysis of human mobility patterns from GPS trajectories and contextual information

This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994).Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. While this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age or gender of the tracked individuals. Further, we explore theorised “third places”, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.PostprintPeer reviewe