Data Mining of Urban Mobility Pattern Using Taxi Trajectory Data

Urban mobility results from human movements from one region to another for interaction such as working, trading goods and other social events. Urban mobility has caused not only urban prosperity, but also some problems in the urban system, such as congestion, air pollution, energy consumption, public health and disease transmission. Therefore, understanding urban mobility is very important for urban planning and management. Urban mobility usually has certain patterns such as origin-destination pattern and spatio-temporal pattern. This project aims to discover the potential mobility patterns based on taxi trajectory data from Shenzhen city, China. Firstly, the entire city is partitioned into several regions. The functions of each region will be studied using a statistical method, and be represented as a vector; Secondly, regions will be clustered into several categories, and regions with similar functions will be grouped together, and finally it aims to discover frequent origin-destination pattern among regions and spatiotemporal pattern based on the probability distribution of taxi pick-up time and location.

Faculty Supervisor:

Xin Wang


Ge Cui






University of Calgary



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