Phithakkitnukoon, Santi; Veloso, Marco; Bento, Carlos; Biderman, Assaf and Ratti, Carlo
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|DOI (Digital Object Identifier) Link:||http://doi.org/10.1007/978-3-642-16917-5_9|
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Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In this paper, we present a predictive model for the number of vacant taxis in a given area based on time of the day, day of the week, and weather condition. The history is used to build the prior probability distributions for our inference engine, which is based on the naïve Bayesian classifier with developed error-based learning algorithm and method for detecting adequacy of historical data using mutual information. Based on 150 taxis in Lisbon, Portugal, we are able to predict for each hour with the overall error rate of 0.8 taxis per sq. km.
|Item Type:||Conference Item|
|Copyright Holders:||2010 Springer-Verlag|
|Extra Information:||Ambient Intelligence
First International Joint Conference, AmI 2010,
Malaga, Spain, November 10-12, 2010.
Editors: Boris de Ruyter, Reiner Wichert, David V. Keyson, Panos Markopoulos, Norbert Streitz, Monica Divitini, Nikolaos Georgantas, Antonio Mana Gomez
Lecture Notes in Computer Science, 6439
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Santi Phithakkitnukoon|
|Date Deposited:||31 Oct 2012 11:28|
|Last Modified:||04 Oct 2016 22:12|
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