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Learning Citywide Patterns of Life from Trajectory Monitoring
We formally define patterns of life analysis as a natural, explainable extension of online unsupervised anomaly detection, where we not only monitor a data stream for anomalies but also explicitly extract normal patterns over time.
Meta-Learning over Time for Destination Prediction Tasks
A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain, including urban planning and management, ride-sharing services, and intelligent transportation systems.
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