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Incremental Frequent Route Based Trajectory Prediction

Published: 05 November 2013 Publication History

Abstract

Recent technological trends enable modern traffic prediction and management systems in which the analysis and prediction of movements of objects is essential. To this extent the present paper proposes IncCCFR---a novel, incremental approach for managing, mining, and predicting the incrementally evolving trajectories of moving objects. In addition to reduced mining and storage costs, a key advantage of the incremental approach is its ability to combine multiple temporally relevant mining results from the past to capture temporal and periodic regularities in movement. The approach and its variants are empirically evaluated on a large real-world data set of moving object trajectories, originating from a fleet of taxis, illustrating that detailed closed frequent routes can be efficiently discovered and used for prediction.

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    cover image ACM Conferences
    IWCTS '13: Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
    November 2013
    92 pages
    ISBN:9781450325271
    DOI:10.1145/2533828
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 05 November 2013

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    Author Tags

    1. Frequent Routes
    2. Incremental Mining
    3. Spatio-Temporal Data Mining
    4. Time Inhomogeneous Trajectory Prediction

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    IWCTS '13 Paper Acceptance Rate 14 of 22 submissions, 64%;
    Overall Acceptance Rate 42 of 57 submissions, 74%

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    • (2022)A Comparative Study of Frequent Pattern Mining with Trajectory DataSensors10.3390/s2219760822:19(7608)Online publication date: 7-Oct-2022
    • (2017)Mining and visual exploration of closed contiguous sequential patterns in trajectoriesInternational Journal of Geographical Information Science10.1080/13658816.2017.139354232:7(1282-1304)Online publication date: 31-Oct-2017
    • (2017)MyWayInformation Systems10.1016/j.is.2015.11.00264:C(350-367)Online publication date: 1-Mar-2017
    • (2015)Scalable Detection of Traffic Congestion from Massive Floating Car Data StreamsProceedings of the 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics10.1145/2835022.2835041(114-121)Online publication date: 3-Nov-2015
    • (2015)Scalable selective traffic congestion notificationProceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems10.1145/2834126.2834134(40-49)Online publication date: 3-Nov-2015
    • (2014)6th ACM SIGSPATIAL International Workshop on Computational Transportation ScienceSIGSPATIAL Special10.1145/2684380.26843836:1(7-10)Online publication date: 24-Oct-2014
    • (2014)Mobility CollectorJournal of Location Based Services10.1080/17489725.2014.9739178:4(229-255)Online publication date: 1-Oct-2014

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