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Modeling and prediction of moving region trajectories

Published: 02 November 2010 Publication History

Abstract

Data about moving objects is being collected in many different application domains with the help of sensor networks, GPS-enabled devices, and in particular airborne sensors and satellites. Such moving objects often represent not just point-based objects, but rather moving regions like hurricanes, oil-spills, or animal herds. One key application feature users are often interested in is the exploration and prediction of moving object trajectories. While there exist models and techniques that help to predict the movement of moving point objects, no such method for moving regions has been proposed yet.
In this paper, we present an approach to model and predict the development of moving regions. Our method not only predicts the trajectory of regions, but also the evolution of a region's spatial extent and orientation. For this, moving regions are modelled using minimum enclosing boxes, and evolution patterns of regions are determined using linear regression and a recursive motion function. We demonstrate the functionality and effectiveness of the proposed technique using real-world sensor data from different application domains.

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  • (2024)Prediction techniques of spatiotemporal trajectory data: progress and frameworkThird International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)10.1117/12.3031312(282)Online publication date: 19-Jul-2024
  • (2022)DeepFR: A trajectory prediction model based on deep feature representationInformation Sciences10.1016/j.ins.2022.05.019604(226-248)Online publication date: Aug-2022
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cover image ACM Conferences
IWGS '10: Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
November 2010
67 pages
ISBN:9781450304313
DOI:10.1145/1878500
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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Publication History

Published: 02 November 2010

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Author Tags

  1. moving regions
  2. prediction
  3. stream processing
  4. trajectories

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Overall Acceptance Rate 7 of 9 submissions, 78%

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Cited By

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  • (2024)Generic Semantic Trajectory Data Modelling Approach based on OntologiesJournal of Information & Knowledge Management10.1142/S021964922450083723:06Online publication date: 3-Aug-2024
  • (2024)Prediction techniques of spatiotemporal trajectory data: progress and frameworkThird International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)10.1117/12.3031312(282)Online publication date: 19-Jul-2024
  • (2022)DeepFR: A trajectory prediction model based on deep feature representationInformation Sciences10.1016/j.ins.2022.05.019604(226-248)Online publication date: Aug-2022
  • (2021)Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an exampleComputational Geosciences10.1007/s10596-021-10037-2Online publication date: 5-Mar-2021
  • (2018)GeoStreamsACM Computing Surveys10.1145/317784851:3(1-37)Online publication date: 23-May-2018
  • (2017)Spatio-temporal dinamic of geographic object by means of trajectories in spatial database2017 12th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI.2017.7975698(1-6)Online publication date: Jun-2017
  • (2017)Regions Trajectories Data: Evolution of Modeling and Construction MethodsIntelligent Interactive Multimedia Systems and Services 201710.1007/978-3-319-59480-4_34(343-352)Online publication date: 28-May-2017
  • (2016)Trend-based prediction of spatial changeProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060770(1074-1080)Online publication date: 9-Jul-2016
  • (2016)Micro-Scale Severe Weather Prediction Based on Region Trajectories Extracted from Meteorological Radar Data2016 IEEE International Conference on Computer and Information Technology (CIT)10.1109/CIT.2016.21(335-338)Online publication date: Dec-2016
  • (2016)Modeling Moving Regions: Colorectal Cancer Case StudyIntelligent Interactive Multimedia Systems and Services 201610.1007/978-3-319-39345-2_36(417-426)Online publication date: 4-Jun-2016
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