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Revealing Taxi Driver's Mobility Intelligence through His Trace

Revealing Taxi Driver's Mobility Intelligence through His Trace

Liang Liu, Clio Andris, Assaf Biderman, Carlo Ratti
Copyright: © 2010 |Pages: 16
ISBN13: 9781615207695|ISBN10: 1615207694|ISBN13 Softcover: 9781616922856|EISBN13: 9781615207701
DOI: 10.4018/978-1-61520-769-5.ch007
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MLA

Liu, Liang, et al. "Revealing Taxi Driver's Mobility Intelligence through His Trace." Movement-Aware Applications for Sustainable Mobility: Technologies and Approaches, edited by Monica Wachowicz, IGI Global, 2010, pp. 105-120. https://doi.org/10.4018/978-1-61520-769-5.ch007

APA

Liu, L., Andris, C., Biderman, A., & Ratti, C. (2010). Revealing Taxi Driver's Mobility Intelligence through His Trace. In M. Wachowicz (Ed.), Movement-Aware Applications for Sustainable Mobility: Technologies and Approaches (pp. 105-120). IGI Global. https://doi.org/10.4018/978-1-61520-769-5.ch007

Chicago

Liu, Liang, et al. "Revealing Taxi Driver's Mobility Intelligence through His Trace." In Movement-Aware Applications for Sustainable Mobility: Technologies and Approaches, edited by Monica Wachowicz, 105-120. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-769-5.ch007

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Abstract

This study develops a methodology for the analysis of taxi drivers’ operation behavior in a real urban environment. The research objective is to spatially and temporally quantify, visualize, and examine taxi drivers’ operational behavior and skill (as measured by income), which the authors call ‘mobility intelligence’. For the first time, taxi drivers’ different operation strategies were systematically analyzed through their daily activity traces. Routes and economic behavior data were collected with the use of Global Positioning System (GPS) and a set of spatiotemporal analysis tools were developed. Drivers are categorized by their daily income into top drivers and ordinary drivers. A 3D clustering technique is used to quantitatively analyze the spatiotemporal patterns for top driver and ordinary driver. Also, fractal analysis is employed to quantify tortuosity of movement paths and to explore how top and ordinary drivers operate on different spatial scales at different times, where the primary focus is to reveal top driver mobility intelligence.

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