Determining user specific semantics of locations extracted from trajectory data

Knowledge about people's daily travel behavior is very relevant for transportation planning, but also for urban and regional planning in general. This information is typically collected through questionnaires or surveys. With the increasing availability of mobile devices capable of using Global Navigation Satellite Systems, it is possible to derive individual mobility behavior on a large scale and for a variety of different users. However, the challenge is to derive the relevant information from the mere GNSS trajectories; in this paper, the relevant information is semantic locations such as home, work place or leisure places. This paper presents an approach to first detect and cluster stop points as potential semantic locations of a user, which are then enriched with Points of Interest from OpenStreetMap and additional features, and finally a Viterbi optimization assigns the most probable semantics to these locations. Overall, this approach produces promising results for predicting user location semantics on a generalized level

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Smartphone sensing methods for studying behavior in everyday life

Human behavior is the focus of many studies in the social, health, and behavioral sciences. Yet, few studies use behavioral observation methods to collect objective measures of behavior as it occurs in daily life, out in the real world — presumably the context of ultimate interest. Here, we provide a review of recent studies focused on measuring human behavior using smartphones and their embedded mobile sensors. To draw attention to current advances in the field of smartphone sensing, we describe the daily behaviors captured using these methods, which include movement behaviors (physical activity, mobility patterns), social behaviors (face-to-face encounters, computer-mediated communications), and other daily activities (non-mediated and mediated activities). We conclude by pointing to promising areas of future research for studies using Smartphone Sensing Methods (SSMs) in the behavioral sciences.This research was supported by National Science Foundation (NSF) Award BCS-1520288

Modelação da exposição a poluentes tóxicos relacionados com o tráfego

Doutoramento em Ciências e Engenharia do AmbienteAtualmente, a poluição atmosférica constitui uma das principais causas ambientais de mortalidade. Cerca de 30% da população europeia residente em áreas urbanas encontra-se exposta a níveis de poluição atmosférica superiores aos valores- limite de qualidade do ar legislados para proteção da saúde humana, representando o tráfego rodoviário uma das principais fontes de poluição urbana. Além dos poluentes tradicionais avaliados em áreas urbanas, os poluentes classificados como perigosos para a saúde (Hazard Air Pollutants - HAPs) têm particular relevância devido aos seus conhecidos efeitos tóxicos e cancerígenos. Neste sentido, a avaliação da exposição tornase primordial para a determinação da relação entre a poluição atmosférica urbana e efeitos na saúde. O presente estudo tem como principal objetivo o desenvolvimento e implementação de uma metodologia para avaliação da exposição individual à poluição atmosférica urbana relacionada com o tráfego rodoviário. De modo a atingir este objetivo, foram identificados os parâmetros relevantes para a quantificação de exposição e analisados os atuais e futuros potenciais impactos na saúde associados com a exposição à poluição urbana. Neste âmbito, o modelo ExPOSITION (EXPOSure model to traffIc-relaTed aIr pOllutioN) foi desenvolvido baseado numa abordagem inovadora que envolve a análise da trajetória dos indivíduos recolhidas por telemóveis com tecnologia GPS e processadas através da abordagem de data mining e análise geoespacial. O modelo ExPOSITION considera também uma abordagem probabilística para caracterizar a variabilidade dos parâmetros microambientais e a sua contribuição para exposição individual. Adicionalmente, de forma a atingir os objetivos do estudo foi desenvolvido um novo módulo de cálculo de emissões de HAPs provenientes do transporte rodoviário. Neste estudo, um sistema de modelação, incluindo os modelos de transporteemissões- dispersão-exposição, foi aplicado na área urbana de Leiria para quantificação de exposição individual a PM2.5 e benzeno. Os resultados de modelação foram validados com base em medições obtidas por monitorização pessoal e monitorização biológica verificando-se uma boa concordância entre os resultados do modelo e dados de medições. A metodologia desenvolvida e implementada no âmbito deste trabalho permite analisar e estimar a magnitude, frequência e inter e intra-variabilidade dos níveis de exposição individual, bem como a contribuição de diferentes microambientes, considerando claramente a sequência de eventos de exposição e relação fonte-recetor, que é fundamental para avaliação dos efeitos na saúde e estudos epidemiológicos. O presente trabalho contribui para uma melhor compreensão da exposição individual em áreas urbanas, proporcionando novas perspetivas sobre a exposição individual, essenciais na seleção de estratégias de redução da exposição à poluição atmosférica urbana, e consequentes efeitos na saúde.Currently, air pollution represents one of the main environmental causes of mortality. About 30% of European citizens in urban areas are exposed to air pollution levels that exceed the air quality limits set by the legislation for the protection of human health, with road transport being the most significant pollution source. In addition to the traditional air pollutants evaluated in urban areas, the hazardous air pollutants (HAPs) has been the subject of particular concern because of their known toxic and carcinogenic effects. In this sense, the evaluation of exposure becomes essential in determining the relationship between urban air pollution and health effects. The main objective of the current study is the development and implementation of a consistent approach for the quantification of individual exposure to trafficrelated air pollutants. For this purpose, relevant parameters of exposure quantification were identified and the current and future potential impacts on human health associated with exposure to urban air pollution were analysed. In this context, the ExPOSITION model (EXPOSure model to traffIc-relaTed aIr) was developed by using a novel approach based on the trajectory analysis of the individuals collected by mobile phones with GPS and processed using the data mining approach and geo-spatial analysis within GIS. Also, the ExPOSITION model considers a probabilistic approach to characterize the variability of microenvironmental parameters and its contribution to personal exposure. Additionally, in order to achieve the objectives of the current study, a new module to quantify emissions of traffic-related HAPs was developed. In this study, a modelling system, including transport-emissions-dispersionexposure models was applied to the Leiria urban area for quantification of individual exposure to PM2.5 and benzene. The modelling results were validated based on measurements obtained by personal monitoring and biological monitoring evidencing a good agreement between the model results and measurement data. The methodology developed and implemented in this work allows to estimate and analyse the magnitude, frequency and the inter and intravariability of personal exposure levels, as well as the contribution of different microenvironments, clearly addressing the sequence of exposure events and source-receptor relationship, which is essential for health impact assessment and epidemiological studies. This research work contributes to a better understanding of individual exposure in urban areas, providing new perspectives on individual exposure, essential in the selection of strategies to reduce exposure to urban air pollution and related health effects

FROM SMALL-WORLDS TO BIG DATA:TEMPORAL AND MULTIDIMENSIONAL ASPECTS OF HUMAN NETWORKS

In this thesis we address the close interplay among mobility, offline relationships and online interactions and the related human networks at different dimensional scales and temporal granularities. By generally adopting a data-driven approach, we move from small datasets about physical interactions mediated by human-carried devices, describing small social realities, to large-scale graphs that evolve over time, as well as from human mobility trajectories to face-to-face contacts occurring in different geographical contexts. We explore in depth the relation between human mobility and the social structure induced by the overlapping of different people's trajectories on GPS traces collected in urban and metropolitan areas. We define the notions of geo-location and geo-community which are operational in describing in a unique framework both spatial and social aspects of human behavior. Through the concept of geo-community we model the human mobility adopting a bipartite graph. Thanks to this graph representation we can generate a social structure that is plausible w.r.t. the real interactions. In general the modeling approach have the merit for reporting the mobility in a graph-theoretic framework making the study of the interplay mobility/sociality more affordable and intuitive. Our modeling approach also results in a mobility model, Geo-CoMM, which lies on and exploits the idea of geo-community. The model represents a particular instance of a general framework we provide. A framework where the social structure behind the preferred-location based mobility models emerges. We validate Geo-CoMM on spatial, temporal, pairwise connectivity and social features showing that it reproduces the main statistical properties observed in real traces. As concerns the offline/online interplay we provide a complete overview of the close connection between online and offline sociality. To reach our goal we gather data about offline contacts and social interactions on Facebook of a group of students and we propose a multidimensional network analysis which allows us to deeply understand how the characteristics of users in the distinct networks impact each other. Results show how offline and Facebook friends are different. This way we confirm and worsen the general intuition that online social networks have shifted away from their original goal to mirror the offline sociality of individuals. As for the role and the social importance, it becomes apparent that social features such as user popularity or community structure do not transfer along social dimensions, as confirmed by our correlation analysis of the network layers and by the comparison among the communities. In the last chapters we analyze the evolution of the online social network from a physical time perspective, i.e. considering the graph evolution as a graph time-series and not as a function of the network basic properties (number of nodes or links). As for the physical time in a user-centric viewpoint, we investigate the bursty nature of the link creation process in online social network. We prove not only that it is a highly inhomogeneous process, but also identify patterns of burstiness common to all nodes. Then we focus on the dynamic formation of two fundamental network building components: dyads and triads. We propose two new metrics to aid the temporal analysis on physical time: link creation delay and triangle closure delay. These two metrics enable us to study the dynamic creation of dyads and triads, and to highlight network behavior that would otherwise remain hidden. In our analysis, we find that link delays are generally very low in absolute time and are largely independent of the dates people join the network. To highlight the social nature of this metric, we introduce the term \textit{peerness} to quantify how well linked users overlap in lifetimes. As for triadic closure delay we first introduce an algorithm to extract of temporal triangle which enables us to monitor the triangle formation process, and to detect sudden changes in the triangle formation behavior, possibly related to external events. In particular, we show that the introduction of new service functionalities had a disruptive impact on the triangle creation process in the network

Periodic pattern mining from spatio-temporal trajectory data

Rapid development in GPS tracking techniques produces a large number of spatio-temporal trajectory data. The analysis of these data provides us with a new opportunity to discover useful behavioural patterns. Spatio-temporal periodic pattern mining is employed to find temporal regularities for interesting places. Mining periodic patterns from spatio-temporal trajectories can reveal useful, important and valuable information about people's regular and recurrent movements and behaviours. Previous studies have been proposed to extract people's regular and repeating movement behavior from spatio-temporal trajectories. These previous approaches can target three following issues, (1) long individual trajectory; (2) spatial fuzziness; and (3) temporal fuzziness. First, periodic pattern mining is different to other pattern mining, such as association rule ming and sequential pattern mining, periodic pattern mining requires a very long trajectory from an individual so that the regular period can be extracted from this long single trajectory, for example, one month or one year period. Second, spatial fuzziness shows although a moving object can regularly move along the similar route, it is impossible for it to appear at the exactly same location. For instance, Bob goes to work everyday, and although he can follow a similar path from home to his workplace, the same location cannot be repeated across different days. Third, temporal fuzziness shows that periodicity is complicated including partial time span and multiple interleaving periods. In reality, the period is partial, it is highly impossible to occur through the whole movement of the object. Alternatively, the moving object has only a few periods, such as a daily period for work, or yearly period for holidays. However, it is insufficient to find effective periodic patterns considering these three issues only. This thesis aims to develop a new framework to extract more effective, understandable and meaningful periodic patterns by taking more features of spatio-temporal trajectories into account. The first feature is trajectory sequence, GPS trajectory data is temporally ordered sequences of geolocation which can be represented as consecutive trajectory segments, where each entry in each trajectory segment is closely related to the previous sampled point (trajectory node) and the latter one, rather than being isolated. Existing approaches disregard the important sequential nature of trajectory. Furthermore, they introduce both unwanted false positive reference spots and false negative reference spots. The second feature is spatial and temporal aspects. GPS trajectory data can be presented as triple data (x; y; t), x and y represent longitude and latitude respectively whilst t shows corresponding time in this location. Obviously, spatial and temporal aspects are two key factors. Existing methods do not consider these two aspects together in periodic pattern mining. Irregular time interval is the third feature of spatio-temporal trajectory. In reality, due to weather conditions, device malfunctions, or battery issues, the trajectory data are not always regularly sampled. Existing algorithms cannot deal with this issue but instead require a computationally expensive trajectory interpolation process, or it is assumed that trajectory is with regular time interval. The fourth feature is hierarchy of space. Hierarchy is an inherent property of spatial data that can be expressed in different levels, such as a country includes many states, a shopping mall is comprised of many shops. Hierarchy of space can find more hidden and valuable periodic patterns. Existing studies do not consider this inherent property of trajectory. Hidden background semantic information is the final feature. Aspatial semantic information is one of important features in spatio-temporal data, and it is embedded into the trajectory data. If the background semantic information is considered, more meaningful, understandable and useful periodic patterns can be extracted. However, existing methods do not consider the geographical information underlying trajectories. In addition, at times we are interested in finding periodic patterns among trajectory paths rather than trajectory nodes for different applications. This means periodic patterns should be identified and detected against trajectory paths rather than trajectory nodes for some applications. Existing approaches for periodic pattern mining focus on trajectories nodes rather than paths. To sum up, the aim of this thesis is to investigate solutions to these problems in periodic pattern mining in order to extract more meaningful, understandable periodic patterns. Each of three chapters addresses a different problem and then proposes adequate solutions to problems currently not addressed in existing studies. Finally, this thesis proposes a new framework to address all problems. First, we investigated a path-based solution which can target trajectory sequence and spatio-temporal aspects. We proposed an algorithm called Traclus (spatio-temporal) which can take spatial and temporal aspects into account at the same time instead of only considering spatial aspect. The result indicated our method produced more effective periodic patterns based on trajectory paths than existing node-based methods using two real-world trajectories. In order to consider hierarchy of space, we investigated existing hierarchical clustering approaches to obtain hierarchical reference spots (trajectory paths) for periodic pattern mining. HDBSCAN is an incremental version of DBSCAN which is able to handle clusters with different densities to generate a hierarchical clustering result using the single-linkage method, and then it automatically extracts clusters from a hierarchical tree. Thus, we modified traditional clustering method DBSCAN in Traclus (spatio-temporal) to HDBSCAN for extraction of hierarchical reference spots. The result is convincing, and reveals more periodic patterns than those of existing methods. Second, we introduced a stop/move method to annotate each spatio-temporal entry with a semantic label, such as restaurant, university and hospital. This method can enrich a trajectory with background semantic information so that we can easily infer people's repeating behaviors. In addition, existing methods use interpolation to make trajectory regular and then apply Fourier transform and autocorrelation to automatically detect period for each reference spot. An increasing number of trajectory nodes leads to an exponential increase of running time. Thus, we employed Lomb-Scargle periodogram to detect period for each reference spot based on raw trajectory without requiring any interpolation method. The results showed our method outperformed existing approaches on effectiveness and efficiency based on two real datasets. For hierarchical aspect, we extended previous work to find hierarchical semantic periodic patterns by applying HDBSCAN. The results were promising. Third, we apply our methodology to a case study, which reveals many interesting medical periodic patterns. These patterns can effectively explore human movement behaviors for positive medical outcomes. To sum up, this research proposed a new framework to gradually target the problems that existing methods cannot handle. These include: how to consider trajectory sequence, how to consider spatial temporal aspects together, how to deal with trajectory with irregular time interval, how to consider hierarchy of space and how to extract semantic information behind trajectory. After addressing all these problems, the experimental results demonstrate that our method can find more understandable, meaningful and effective periodic patterns than existing approaches