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Discovering Regional Taxicab Demand Based on Distribution Modeling from Trajectory Data

Computational Science and Engineering(2014)

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Abstract
Taxicab demand discovering is one of the most fundamental issues of taxicab services. Most of the regions in one city suffer the demand and supply disequilibrium problem. It causes the difficulty in scheduling taxicabs for taxicab companies. It will be solved by modeling the regional demand of taxicabs by using trajectory data. In this paper, we propose a method to model regional taxicab demand. Firstly, the method uses the KS measures to test the distribution of taxicab service rate. Then, it uses the Parzen window to estimate the probability density function of the rate. We have implemented our method with experiments based on real trajectory data. The results show the effectiveness of our method.
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Key words
taxicab services,trajectory data,scheduling,probability density function,taxicab demand,demand and supply disequilibrium problem,regional taxicab demand,public transport,supply and demand,parzen window,distribution modeling,probability,estimation,testing,trajectory,global positioning system,kernel,gaussian distribution
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