Abstract
Data produced by a transportation system is inevitably growing ever larger. Thus, exploiting the data for analytic purpose is required to comprehend the salient pattern and to improve transportation system itself. This paper presents a solution towards finding frequent routes from taxi trip with certain time windows. MapReduce approach is used to tackle enormous data processing of taxi trips. In the meantime, quadrant-based partition and hashing technique are proposed to reduce the computation time while searching the frequent routes. The application of the proposed approach is demonstrated using the real taxi trip data around New York City.
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