EUROMICRO '03 Proceedings of the 29th Conference on EUROMICRO
Early warning systems in practice: performance of the SAFE system in the field
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Using mobile phones to determine transportation modes
ACM Transactions on Sensor Networks (TOSN)
Cooperative transit tracking using smart-phones
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Transportation mode detection using mobile phones and GIS information
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Context- and situation-awareness in information logistics
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Least squares quantization in PCM
IEEE Transactions on Information Theory
Privacy-Preserving Sharing of Sensitive Semantic Locations under Road-Network Constraints
MDM '12 Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (mdm 2012)
Analysis of user mobility data sources for multi-user context modeling
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
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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.