A Method for Analyzing Pick-Up/Drop-Off Distribution of Taxi Passengers' in Urban Areas Based on Dynamical Network View

To analyze taxi origin-destination (OD) trips data and understand passengers flow patterns from the pick-up/drop-off distribution, is beneficial to many applications in taxi dispatching systems. Mapping the pick-up/drop-off locations onto taxi zones maps, relations between zones would come into being when zones are geographical adjacent, or exchanging many passengers, or sharing similar pick-up/drop-off patterns. We categorize these relations between zones as geo-neighbor, complementary and homogenous respectively. An analytic method based on dynamical network view to detect communities of the latter two relations is proposed in this paper. It makes an analogy between zones and words and performs a distributed representation learning method, mapping zones to a dense low-dimensional vector space where closely related zones (complementary/homogenous) are close. Then, community detection is easily performed by using the cosine of two vectors as a measurement. With New York City taxi trips data as a case of study and making a comparison between workdays and weekends, experiments based on the proposed method show interesting results.

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