Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory
Rahul Deb Das, Stephan Winter
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION | MDPI | Published : 2016
Transport mode information is essential for understanding people's movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short i..View full abstract
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Awarded by Australian Research Council
This research has been supported by the Australian Research Council (LP120200130).