13,366 research outputs found
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
Mobility Data Mining for Rural and Urban Map-Matching
Ajalis-ruumiliste andmete kogumine on hoogustunud erinevates rakendustes ja seadmetes. Globaalne positsiooneerimise süsteem (GPS) on kõige populaarsem viis asukoha teave saamiseks. Kaardipunktide vastavusse seadmine on konseptsioon, mis püüab GPS andmeid trajektooris viia vastavusse reaalse teedevõrguga. GPS andmete suurim probleem tuleneb andmete mõõtmis-ja kogumisvigadest ja nende parandamine on suur väljakutse. Käesoleva lõputöö eesmärk on arendada andmete töötlusvoo ja visualiseerimise raamistik muutmaks GPS punktid loogilisteks trajektoorideks ja vigaste GPS punktide asukohtade parandamiseks. Selle eesmärgi saavutamiseks tutvustatakse uut lähenemist trajektooride mustrite leidmiseks.The functionality of gathering spatio-temporal data has seen increasing usage in various applications and devices. The Global Positioning System (GPS) is a satellite navigation system which is mostly used for gathering location information. Map-matching is the procedure of matching trajectories from a sequence of raw GPS data points to the appropriate road networks. GPS data errors are one of the biggest problems and correcting them is a big challenge. The main goal of this thesis work is to build a data pipeline and visualization framework for turning raw GPS data to trajectories and correcting erroneous GPS points by new map-matching approach. For achieving the goal a new approach for trajectory pattern mining is introduced
PATH: Person Authentication using Trace Histories
In this paper, a solution to the problem of Active Authentication using trace
histories is addressed. Specifically, the task is to perform user verification
on mobile devices using historical location traces of the user as a function of
time. Considering the movement of a human as a Markovian motion, a modified
Hidden Markov Model (HMM)-based solution is proposed. The proposed method,
namely the Marginally Smoothed HMM (MSHMM), utilizes the marginal probabilities
of location and timing information of the observations to smooth-out the
emission probabilities while training. Hence, it can efficiently handle
unforeseen observations during the test phase. The verification performance of
this method is compared to a sequence matching (SM) method , a Markov
Chain-based method (MC) and an HMM with basic Laplace Smoothing (HMM-lap).
Experimental results using the location information of the UMD Active
Authentication Dataset-02 (UMDAA02) and the GeoLife dataset are presented. The
proposed MSHMM method outperforms the compared methods in terms of equal error
rate (EER). Additionally, the effects of different parameters on the proposed
method are discussed.Comment: 8 pages, 9 figures. Best Paper award at IEEE UEMCON 201
DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams
Similarity matching and join of time series data streams has gained a lot of
relevance in today's world that has large streaming data. This process finds
wide scale application in the areas of location tracking, sensor networks,
object positioning and monitoring to name a few. However, as the size of the
data stream increases, the cost involved to retain all the data in order to aid
the process of similarity matching also increases. We develop a novel framework
to addresses the following objectives. Firstly, Dimension reduction is
performed in the preprocessing stage, where large stream data is segmented and
reduced into a compact representation such that it retains all the crucial
information by a technique called Multi-level Segment Means (MSM). This reduces
the space complexity associated with the storage of large time-series data
streams. Secondly, it incorporates effective Similarity Matching technique to
analyze if the new data objects are symmetric to the existing data stream. And
finally, the Pruning Technique that filters out the pseudo data object pairs
and join only the relevant pairs. The computational cost for MSM is O(l*ni) and
the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction
Factor. We have performed exhaustive experimental trials to show that the
proposed framework is both efficient and competent in comparison with earlier
works.Comment: 20 pages,8 figures, 6 Table
Moving Object Trajectories Meta-Model And Spatio-Temporal Queries
In this paper, a general moving object trajectories framework is put forward
to allow independent applications processing trajectories data benefit from a
high level of interoperability, information sharing as well as an efficient
answer for a wide range of complex trajectory queries. Our proposed meta-model
is based on ontology and event approach, incorporates existing presentations of
trajectory and integrates new patterns like space-time path to describe
activities in geographical space-time. We introduce recursive Region of
Interest concepts and deal mobile objects trajectories with diverse
spatio-temporal sampling protocols and different sensors available that
traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4,
No.2, April 201
Context Trees: Augmenting Geospatial Trajectories with Context
Exposing latent knowledge in geospatial trajectories has the potential to
provide a better understanding of the movements of individuals and groups.
Motivated by such a desire, this work presents the context tree, a new
hierarchical data structure that summarises the context behind user actions in
a single model. We propose a method for context tree construction that augments
geospatial trajectories with land usage data to identify such contexts. Through
evaluation of the construction method and analysis of the properties of
generated context trees, we demonstrate the foundation for understanding and
modelling behaviour afforded. Summarising user contexts into a single data
structure gives easy access to information that would otherwise remain latent,
providing the basis for better understanding and predicting the actions and
behaviours of individuals and groups. Finally, we also present a method for
pruning context trees, for use in applications where it is desirable to reduce
the size of the tree while retaining useful information
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
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 electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on
Intelligent Transportation System
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