Searching for similar trajectories in spatial networks

Tiakas, Eleftherios; Papadopoulos, Apostolos N.; Νανόπουλος, Αλέξανδρος; Manolopoulos, Yannis; Stojanović, Dragan; Djordjević‐Kajan, Slobodanka

doi:10.1016/j.jss.2008.11.832

Cited by 63 publications

(66 citation statements)

References 24 publications

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“…When the trajectories are not free but constrained by a network, like a road network for cars' trajectories, new density and distance measures have to take into account the specificity of the network. Usually, distance measures are no longer based on the Euclidian distance but on algorithms for finding the shortest path in a graph [Tiakas et al 2009]. In [Han et al 2012] the authors find clusters of segments of cars' trajectories by looking for sequences of contiguous road segments that are followed by continuous traffic flows.…”

Section: General Characteristics For Trajectory Knowledge Discoverymentioning

confidence: 99%

Semantic trajectories modeling and analysis

et al. 2013

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___________________________________________________________________Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for i) constructing trajectories from movement tracks, ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the paper surveys the new privacy issues that rise due to the semantic aspects of trajectories.

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Section: General Characteristics For Trajectory Knowledge Discoverymentioning

confidence: 99%

Semantic trajectories modeling and analysis

et al. 2013

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“…On the other hand, trying to incorporate the complexity of real trajectories, hierarchical clustering algorithms build models by introducing global or local variables, such as the speed, duration, curvature, and other descriptors of trajectories [20][21][22][23][24]. Mohammad et al showed the extraction of new point features: bearing rate, the rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles, enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores [25].…”

Section: Trajectory Clustering Methodsmentioning

confidence: 99%

A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data

Zhang

Chen

et al. 2018

IJGI

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Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines, which is useful in many areas such as transportation, urban planning, and migration studies. In this paper, we present a new Simple Line Clustering Method (SLCM) that was designed to discover the strongest spatial linkage by searching for neighboring lines for every OD trip within a certain radius. This method adopts entropy theory and the probability distribution function for parameter selection to ensure significant clustering results. We demonstrate this method using bike-sharing location data in a metropolitan city. Results show that (1) the SLCM was significantly effective in discovering clusters at different scales, (2) results with the SLCM analysis confirmed known structures and discovered unknown structures, and (3) this approach can also be applied to other OD data to facilitate pattern extraction and structure understanding.

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“…Performing spatiotemporal queries on those data becomes crucial in supporting various types of advanced analysis and decision-making [2]. Among those queries, identifying similar trajectories is one of the most popular query types because of its applications in numerous fields, such as identifying traffic congestions, summarizing human mobility patterns and planning for emergency routes [3]. Scientists have formulated various similarity measures according to the needs of specific domain applications [4].…”

Section: Introductionmentioning

confidence: 99%

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Wang

Zhang

et al. 2018

IJGI

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Performing similarity analysis on trajectories consisting of massive numbers of tracking points is computationally challenging. We introduce a progressive minimum bounding rectangle (MBR) and minimum distance (MINDIST) approach to process the K Best Connected Trajectory (K-BCT) query, which aims to find the top K similarity trajectories to a given query trajectory. Our approach has three unique features to speed up the query. First, the approach builds a series of progressive MBRs from the query trajectory to determine the order of reference trajectories to identify the target top K reference trajectories at an earlier stage. Second, this method introduces a grid-based search method to speed up the matched point detection between two trajectories for similarity measures. Third, this approach further leverages the many-core computing power of Graphical Processing Unit (GPU) devices to perform the query in a parallel manner. We have conducted tests with ship tracking data and human movement data using GPU instances from Amazon Web Services. Preliminary results indicate that (a) parallel computing has greatly improved the efficiency of the query, and (b) our optimized approach can further speedup the computation compared to parallel implementations.

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Searching for similar trajectories in spatial networks

Cited by 63 publications

References 24 publications

Semantic trajectories modeling and analysis

Semantic trajectories modeling and analysis

A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

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