Abstract
Customized bus (CB) services that operate between residential areas and work places during rush hours have surfaced in many cities to deal with traffic congestion problem. One major challenge of CB services is to design service areas based on user demands. To solve this problem, we extract commute patterns from large-scale taxi GPS data for CB service area design. We present a novel clustering algorithm that embeds origin and destination data to a low dimensional feature space, such that the resulting patterns satisfy both the needs of commuters and operators. The algorithm has been tested on both synthesized and real-world data.
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