Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City
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Ratti_Taxi-aware map.pdf
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Author(s)
Phithakkitnukoon, Santi
Veloso, Marco
Bento, Carlos
Biderman, Assaf
Ratti, Carlo
Date Issued
2010
Journal
Ambient Intelligence
Publisher
Springer-Verlag
Citation
Phithakkitnukoon, Santi, Marco Veloso, Carlos Bento, Assaf Biderman, and Carlo Ratti. “Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City.” Ambient Intelligence (2010): 86–95.
Version
Author's final manuscript
Abstract
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 1x1 km[superscript 2] area.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. SENSEable City Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-642-16917-5_9
