Abstract
Different taxi drivers may use different strategies to choose operating regions and find customers, which is called mobility intelligence. In this paper, we present a visualization system to analyze a large amount of spatial-temporal multi-dimensional trajectory data and identify some key factors that differentiate the top drivers and ordinary drivers according to their income. Two novel encoding schemes, Choice-of-Location graph and Move/Wait Strategy tree, have been proposed to analyze drivers’ behaviors when choosing operating locations and drivers’ move/wait strategies when their taxis are vacant.We have applied our system to the trajectories of thousands of taxis in a major city and have gained some interesting findings on taxi drivers’ mobility intelligence.
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Gao, Y., Xu, P., Lu, L., Liu, H., Liu, S., Qu, H. (2012). Visualization of Taxi Drivers’ Income and Mobility Intelligence. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_27
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DOI: https://doi.org/10.1007/978-3-642-33191-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33190-9
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