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

: Mehta, Paras; Müller, Sebastian; Voisard, Agnes


Pfoser, Dieter (Ed.); Voisard, Agnes (Ed.) ; Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Spatial Information -SIGSPATIAL-:
GEOCROWD 2013, Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information. Proceedings : 05. - 08. November 2013, Orlando, Florida, USA
New York: ACM, 2013
ISBN: 978-1-4503-2528-8
International Workshop on Crowdsourced and Volunteered Geographic Information (GEOCROWD) <2, 2013, Orlando/Fla.>
Fraunhofer FOKUS ()
GIS; disaster response; early warning; clustering; targeted alerting; pattern recognition; context awareness

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.