Mining Spatio-Temporal Reachable Regions over Massive Trajectory Data

Mining spatio-temporal reachable regions aims to find a set of road segments from massive trajectory data, that are reachable from a user-specified location and within a given temporal period. Accurately extracting such spatio-temporal reachable area is vital in many urban applications, e.g., (i) location-based recommendation, (ii) location-based advertising, and (iii) business coverage analysis. The traditional approach of answering such queries essentially performs a distance-based range query over the given road network, which have two main drawbacks: (i) it only works with the physical travel distances, where the users usually care more about dynamic traveling time, and (ii) it gives the same result regardless of the querying time, where the reachable area could vary significantly with different traffic conditions. Motivated by these observations, in this thesis, we propose a data- driven approach to formulate the problem as mining actual reachable region based on real historical trajectory dataset. The main challenge in our approach is the system efficiency, as verifying the reachability over the massive trajectories involves huge amount of disk I/Os. In this thesis, we develop two indexing structures: 1) spatio-temporal index (ST-Index) and 2) connection index (Con-Index) to reduce redundant trajectory data access operations. We also propose a novel query processing algorithm with: 1) maximum bounding region search, which directly extracts a small searching region from the index structure and 2) trace back search, which refines the search results from the previous step to find the final query result. Moreover, our system can also efficiently answer the spatio-temporal reachability query with multiple query locations by skipping the overlapped area search. We evaluate our system extensively using a large-scale real taxi trajectory data in Shenzhen, China, where results demonstrate that the proposed algorithms can reduce 50%-90% running time over baseline algorithms

Extract human mobility patterns powered by City Semantic Diagram

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Placement and Movement Episodes Detection using Mobile Trajectories Data

Teostatud töö eesmärgiks on tuvastada asukohaandmetest seisu- ning liikumisepisoode kasutades selleks trajektoori ülekattuvusmaatriksit. Antud töös kasutatud andmed on väga hajusad nii ajalises kui ka geograafilises mõttes. Seetõttu on antud ülesanne suur väljakutse. Välja pakutud lahenduse raames teostati andmeanalüüs mille raames tuvastati kasutajatele tähtsad asukohad ning pakuti välja algoritm, mille abil tuvastda seisu- ning liikumisepisoodid. Andmete analüüsimiseks ning visualiseerimiseks kasutati R-i.This thesis presents a trajectory episode matrix to enable the detection of placement and movement episodes from mobile location data. The data used in this work is very sparse in time and space. Therefore, the estimation of user’s placement and movement patterns poses a big challenge. The presented approach performs data analysis to find meaningful locations and introduces an algorithm to detect movement and placement episodes. To perform the analysis and visualize the results a statistical analysis tool was developed with R. The work done as a result of this thesis can be used to improve the identification of the meaningful locations and to help predicting the semantic meanings of mobile user’s patterns

Predictive task assignment in spatial crowdsourcing: A data-driven approach

With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive Task Assignment (PTA), which aims to maximize the number of assigned tasks by taking into account both current and future workers/tasks that enter the system dynamically with location unknown in advance. We propose a two-phase data-driven framework. The prediction phase hybrids different learning models to predict the locations and routes of future workers and designs a graph embedding approach to estimate the distribution of future tasks. In the assignment component, we propose both greedy algorithm for large-scale applications and optimal algorithm with graph partition based decomposition. Extensive experiments on two real datasets demonstrate the effectiveness of our framework

Profiling and Grouping Space-time Activity Patterns of Urban Individuals

No abstract

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

SEGMENTATION TECHNIQUES BASED ON CLUSTERING FOR THE ANALYSIS OF MOBILITY DATA

La Tesi riguarda l'analisi e applicazione di metodi di segmentazione per il partizionamento delle traiettorie spaziali in sotto-traiettorie semanticamente significative, e il loro utilizzo per l'analisi del comportamento di oggetti in movimento. Le traiettorie spaziali sono dati strutturati complessi costituiti da sequenze ordinate di punti spazio-temporali che campionano il movimento continuo di un oggetto in uno spazio di riferimento. Le tecniche di segmentazione sono essenziali per l'analisi delle traiettorie spaziali. In generale, l'attivit\ue0 di segmentazione divide una sequenza di punti dati in una serie di sottosequenze disgiunte basate su criteri di omogeneit\ue0. La Tesi si focalizza, in particolare, sulle tecniche di segmentazione basate su \u201cdensity based clustering\u201d. A differenza dei processi di clustering tradizionali, che sono applicati ad \u201cinsiemi\u201d di punti, le tecniche di segmentazione basate su clustering partizionano \u201csequenze\u201d in una serie di \u201cclusters\u201d temporalmente separati. Possibili applicazioni includono l'analisi del movimento di individui in ambito urbano e lo studio del comportamento di animali. Alcune tecniche di segmentazione basate su \u201ccluster\u201d sono descritte in letteratura, tuttavia nessuna di queste soluzioni permette di gestire in modo efficace i punti non strutturati (noise). Inoltre, le metodologie adottate per validare queste tecniche soffrono di gravi limitazioni, ad esempio le verifiche sperimentale utilizzano dati molto semplici che non riflettono la complessit\ue0 del movimento reale, come pure non permettono di effettuare un confronto con ground truth. Questa Tesi si focalizza su una recente tecnica per la segmentazione basata su cluster con noise, chiamata SeqScan, proposta in un lavoro precedente. In particolare, la ricerca ha affrontato i seguenti problemi: i) definizione di un framework rigoroso per l' analisi delle propriet\ue0 del modello di segmentazione; ii) validazione del metodo attraverso un'ampia sperimentazione che prevede il confronto con la ground truth; iii) estensione dell'approccio per consentire la individuazione di gatherings. Il gathering \ue9 un gruppo di oggetti mobili che condividono la stessa zona, per un certo periodo di tempo con la possibilit\ue0 di assenze occasionali; iv) sviluppo di una piattaforma software che integra i diversi algoritmi ed ulteriori strumenti a supporto dell'analisi dei dati di mobilit\ue0.The Thesis focuses on segmentation methods for the partitioning of spatial trajectories in semantically meaningful sub-trajectories and their application to the analysis of mobility behavior. Spatial trajectories are complex structured data consisting of sequences of temporally ordered spatio-temporal points sampling the continuous movement of an object in a reference space. Spatial trajectories can reveal behavioral information about individuals and groups of individuals, and that motivates the concern for data analysis techniques. Segmentation techniques are key for the analysis of spatial trajectories. In general, the segmentation task partitions a sequence of data points in a series of disjoint sub-sequences based on some homogeneity criteria. The Thesis focuses, in particular, on the use of clustering methods for the segmentation of spatial trajectories. Unlike the traditional clustering task, which is applied to sets of data points, the goal of this class of techniques is to partition sequential data in temporally separated clusters. Such techniques can be utilized for example to detect the sequences of places or regions visited by moving objects. While a number of techniques for the cluster-based segmentation are proposed in literature, none of them is really robust again noise, while the methodologies put in place to validate those techniques suffer from severe limitations, e.g., simple datasets, no comparison with ground truth. This Thesis focuses on a recent cluster-based segmentation method, called SeqScan, proposed in previous work. This technique promises to be robust against noise, nonetheless the approach is empirical and lacks a formal and theoretical framework. The contribution of this research is twofold. First it provides analytical support to SeqScan, defining a rigorous framework for the analysis of the properties of the model. The method is validated through an extensive experimentation conducted in an interdisciplinary setting and contrasting the segmentation with ground truth. The second contribution is the proposal of a technique for the discovery of a collective pattern, called gathering. The gathering pattern describes a situation in which a significant number of moving objects share the same region, for enough time periods with possibility of occasional absences, e.g. a concert, an exhibition. The technique is built on SeqScan. A platform, called MigrO, has been finally developed, including not only the algorithms but also a variety of tools facilitating data analysis

Visually driven analysis of movement data by progressive clustering

The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, that is, sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, (1) is difficult to build, (2) would require much time to compute, and (3) might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of ‘cheap’ distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real data set. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories

SPATIO-TEXTUAL TRAJECTORIES: MODELS AND APPLICATIONS

L'argomento della Tesi riguarda la gestione e analisi di dati di mobilit\ue0 . La pervasivit\ue0 delle tecnologie per la geolocalizzazione, sensori e reti di comunicazione, ha reso possibile l\u2019acquisizione di grandi quantit\ue0 di dati sul movimento di oggetti, da cui la necessit\ue0 di tecniche che consentano l\u2019organizzazione e l\u2019accesso efficiente ai dati. Una classe importante di dati di mobilit\ue0 \ue8 costituita dalle traiettorie spaziali. Una traiettoria spaziale descrive il movimento continuo di un oggetto in uno spazio di riferimento, e.g. spazio Euclideo, tramite un sequenza di punti campione, temporalmente annotati e ordinati. Le traiettorie spaziali possono descrivere ad esempio il movimento di veicoli, individui o animali, equippaggiati con un ricevitore GPS. Le traiettorie spaziali, tuttavia, descrivono il movimento unicamente in termini di posizione mentre non permettono di descrivere l\u2019evoluzione nel tempo del contesto piu\u2019 ampio in cui il movimento ha luogo. I dati di contesto possono essere acquisiti direttamente dall\u2019ambiente, ad esempio tramite l\u2019uso di sensori, oppure ottenuti dall\u2019applicazione di tecniche analitiche. In questo senso le traiettorie spaziali difettano di espressivit\ue0. Un passo importante nella direzione di modelli piu\u2019 ricchi ed espressivi \ue8 costituito dal modello delle traiettorie \u2018simboliche\u2019. Questo modello permette di descrivere sequenze di attivit\ue0 tramite etichette, ognuna annotata da un intervallo temporale. L\u2019aspetto piu\u2019 innovativo del modello riguarda lo sviluppo di un linguaggio di interrogazione basato su pattern matching. Anche questa soluzione presenta tuttavia importanti limiti perch\ue8 le traiettorie simboliche sono completamente ortogonali a quelle spaziali e quindi ignorano il dato di posizione. Questa Tesi affronta il problema della integrazione della dimensione spaziale con quella simbolica per un accesso efficiente a database di traiettorie denominate spazio-testuali. Il contributo piu\u2019 significativo e\u2019 un sistema per la indicizzazione di traiettorie spazio-testuali a supporto delle interrogazioni di tipo Sequenced Queries, ossia queries espresse come sequenze ordinate di queries elementari spazio-testo. Il sistema si chiama IRWI, e\u2019 un sistema di indici ibrido che combina un indice basato su R-tree per la indicizzazione dei segmenti di traiettoria con un indice basato su inverted file per la indicizzazione dela componente testuale. Il processamento delle query viene effettuato in parallelo valutando le singole queries della sequenza e poi alla fine ricomponendo le sequenze. Un secondo aspetto trattato nella tesi riguarda lo studio di tecniche per l\u2019analisi dei dati di movimento. L\u2019obiettivo \ue8 quello di estrarre pattern comportamentali dalle traiettorie spaziali per poi rappresentarli in termini di traiettorie spazio-testuali. Il contributo pi\uf9 significativo \ue8 la definizione di un algoritmo di segmentazione delle traiettorie che sfrutta tecniche di clustering con un nuovo modello di noise. In ultimo, \ue8 stato realizzato un caso di studio che illustra e riassume la metodologia proposta per l\u2019 analisi e rappresentazione del movimento. La tecnica di segmentazione di cui sopra \ue8 stata utilizzata per l\u2019estrazione da traiettorie spaziali del comportamento migratorio di animali equipaggiati con collari GPS. Questa conoscenza \ue8 stata poi espressa in termini di traiettorie spazio-testuali. Complessivamente, i risultati della ricerca hanno dato luogo a diverse pubblicazioni in riviste e a conferenze.The Thesis concerns the management and analysis of mobility data. The pervasiveness of geo-positioning technologies, sensors and communication networks has led to the collection of large amounts of data on the movement of objects. A major issue is thus how to effectively organize and access such a data. An important category of mobility data is that of spatial trajectories. A spatial trajectory describes the continuous movement of an object in a reference space, e.g. the Euclidean plane, through a set of temporally annotated and ordered sample points. Spatial trajectories can represent the movement of vehicles, people and animals, for example equipped with a GPS receiver. Yet, spatial trajectories can represent the movement exclusively in terms of locations, thus the evolution of the context in which the movement takes place is ignored. In general, the contextual data can be acquired directly from the environment, for example through the use of sensors, or be the result of an analytical process. In this sense, spatial trajectories lack expressivity. An important step towards the specification of richer and more expressive data models, is the symbolic trajectories data model. This model allows for the representation of sequences of activities (or labels), each annotated with a time period. A major novelty of the model is the query language that is based on pattern matching. Nevertheless also this solution presents important limitations because the notion of symbolic trajectory is orthogonal to that of spatial trajectory and thus does not include any location information. The Thesis addresses the problem of integrating the spatial dimension with the symbolic one, providing as well a mechanism enabling the efficient access to a database of spatio-textual trajectories. The major contribution of this research is the proposal of a framework for the indexing of spatio-textual trajectories The goal of the index is to support the processing of queries taking the form of Sequenced queries, that is complex queries expressed as sequences of ordered simple spatio-textual queries. The index is called IRWI. The system is hybrid in that it combines an R-tree for the indexing of spatial trajectory segments with inverted files for the indexing of the textual part. A Sequenced query is next processed in parallel, evaluating first every single query of the sequence, and finally analyzing and recomposing the sequence. A related though different topic of the Thesis regards the study of techniques for mobility data analysis. The objective is to extract behavioral patterns from spatial trajectories and next represent them in terms of spatio-textual trajectories. The major contribution is the definition of an algorithm for the segmentation of the trajectories based on clustering and relying on a novel model of noise. Finally, a case study illustrates and summarizes the methodology proposed for the analysis and representation of mobility data. Specifically the above segmentation technique is used for the extraction of the migratory behavior of a group of animals equipped with GPS collars. Next such a knowledge is encoded in terms of spatio-textual trajectories. The results of this research, spanning data representation and analysis, have been presented in conferences and journals

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestion, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, yet it is very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix-computation-based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps: (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes, and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification, and correlation into a unified and efficient analytical framework. We validated the proposed methodology under both synthetic and real-world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers, and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity, and spatial constraint are also discussed. Document type: Articl