research-article

MoveSafe: a framework for transportation mode-based targeted alerting in disaster response

Authors:

Sebastian Müller,

Agnès VoisardAuthors Info & Claims

GEOCROWD '13: Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

Pages 15 - 22

https://doi.org/10.1145/2534732.2534735

Published: 05 November 2013 Publication History

Abstract

Disasters, whether natural or man-made, can occur in an unexpected and unanticipated manner causing damage and disruptions. In the event of sudden onset of a hazard, private and public transport users and pedestrians need to be informed and guided to safety. Targeted alerting in early warning systems involves the communication of personalized information to a variety of communities based on their different needs and situations to improve alert usability and compliance. In this paper, we present MoveSafe, a generic and extensible framework for transportation mode-based dynamic partitioning of a population for targeted alerting and for better transport management in hazard occurrence scenarios. We infer the transportation mode of the users dynamically using their location traces through continuous feature extraction and maintenance. In combination with the hazard location, we use the transportation mode information to find clusters of people at potentially different levels of risk and with different information needs. The framework also supports a variety of classification features, classifiers, clustering dimensions, and clustering algorithms. We evaluate its performance in different settings and present the results.

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Cited By

Kaur MKaur PSood S(2022)Analytical mapping on trends of information technology in hydrometeorological disasters researchGeocarto International10.1080/10106049.2022.208700637:26(14171-14197)Online publication date: 16-Jun-2022
https://doi.org/10.1080/10106049.2022.2087006
Kaur MKaur PSood S(2022)ICT in disaster management context: a descriptive and critical reviewEnvironmental Science and Pollution Research10.1007/s11356-022-21475-529:57(86796-86814)Online publication date: 7-Jul-2022
https://doi.org/10.1007/s11356-022-21475-5
Cuong TMehta PVoisard A(2015)DOOR: A Data Model for Crowdsourcing with Application to Emergency ResponseInternet of Things. IoT Infrastructures10.1007/978-3-319-19743-2_37(265-270)Online publication date: 26-Jun-2015
https://doi.org/10.1007/978-3-319-19743-2_37

Index Terms

MoveSafe: a framework for transportation mode-based targeted alerting in disaster response
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems

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Published In

cover image ACM Conferences

GEOCROWD '13: Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

November 2013

102 pages

ISBN:9781450325288

DOI:10.1145/2534732

Editors:
Dieter Pfoser
George Mason University
,
Agnès Voisard
Freie Universität Berlin and Fraunhofer FOKUS, Germany

Copyright © 2013 ACM.

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 the author(s) 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: 05 November 2013

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Sixth Framework Programme

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SIGSPATIAL'13

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SIGSPATIAL'13: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5, 2013

Florida, Orlando

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GEOCROWD '13 Paper Acceptance Rate 12 of 20 submissions, 60%;

Overall Acceptance Rate 17 of 30 submissions, 57%

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Cited By

Kaur MKaur PSood S(2022)Analytical mapping on trends of information technology in hydrometeorological disasters researchGeocarto International10.1080/10106049.2022.208700637:26(14171-14197)Online publication date: 16-Jun-2022
https://doi.org/10.1080/10106049.2022.2087006
Kaur MKaur PSood S(2022)ICT in disaster management context: a descriptive and critical reviewEnvironmental Science and Pollution Research10.1007/s11356-022-21475-529:57(86796-86814)Online publication date: 7-Jul-2022
https://doi.org/10.1007/s11356-022-21475-5
Cuong TMehta PVoisard A(2015)DOOR: A Data Model for Crowdsourcing with Application to Emergency ResponseInternet of Things. IoT Infrastructures10.1007/978-3-319-19743-2_37(265-270)Online publication date: 26-Jun-2015
https://doi.org/10.1007/978-3-319-19743-2_37

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