Title: Context-aware taxi demand hotspots prediction

Authors: Han-wen Chang, Yu-chin Tai, Jane Yung-jen Hsu

Addresses: Department of Computer Science and Information Engineering, National Taiwan University, Taiwan. ' Department of Computer Science and Information Engineering, National Taiwan University, Taiwan. ' Department of Computer Science and Information Engineering, National Taiwan University, Taiwan

Abstract: In an urban area, the demand for taxis is not always matched up with the supply. This paper proposes mining historical data to predict demand distributions with respect to contexts of time, weather, and taxi location. The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms are compared and demonstrated in a web mash-up application to show that context-aware demand prediction can help improve the management of taxi fleets.

Keywords: hotspot prediction; data mining; clustering algorithms; taxis; historical data; data filtering; semantic annotation; hotness calculation; demand distribution; time factors; weather; location; web mash-ups; fleet management; business intelligence; Taiwan; supply and demand.

DOI: 10.1504/IJBIDM.2010.030296

International Journal of Business Intelligence and Data Mining, 2010 Vol.5 No.1, pp.3 - 18

Available online: 14 Dec 2009 *

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