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- W2889352281 abstract "With the ubiquity of smartphone devices., taxi dispatching applications have been prevailing in recent years, enabling us to travel effortlessly as per our needs. In this paper, we study the problem of taxi dispatching optimization. Specifically, we develop a novel clustering based scheme that can exploit multi-source information, such as trajectory-based features and road features, for taxi pick-up points recommendations. Our scheme is based on two observations. First, taxi's pick-up points can be influenced by their spatiotemporal clustering. Second, human mobility patterns can reflect the popularity of waiting areas to a certain degree. We carry out our method on historical trajectories generated by over 23,000 taxis during one year. The experimental results validate the effectiveness of the proposed method." @default.
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- W2889352281 date "2017-12-01" @default.
- W2889352281 modified "2023-09-27" @default.
- W2889352281 title "Exploiting Optimization Mechanism for Pick-up Points Recommendations" @default.
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- W2889352281 doi "https://doi.org/10.1109/iccsec.2017.8446836" @default.
- W2889352281 hasPublicationYear "2017" @default.
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