Wait time prediction for airport taxis using weighted nearest neighbor regression

Saiedur Rahaman, M, Ren, Y, Hamilton, M and Salim, F 2018, 'Wait time prediction for airport taxis using weighted nearest neighbor regression', IEEE Access, vol. 6, pp. 74660-74672.


Document type: Journal Article
Collection: Journal Articles

Title Wait time prediction for airport taxis using weighted nearest neighbor regression
Author(s) Saiedur Rahaman, M
Ren, Y
Hamilton, M
Salim, F
Year 2018
Journal name IEEE Access
Volume number 6
Start page 74660
End page 74672
Total pages 13
Publisher IEEE
Abstract In this paper, we address the neighborhood identification problem in the presence of a large number of heterogeneous contextual features. We formulate our research as a problem of queue wait time prediction for taxi drivers at airports and investigate heterogeneous factors related to time, weather, flight arrivals, and taxi trips. The neighborhood-based methods have been applied to this type of problem previously. However, the failure to capture the relevant heterogeneous contextual factors and their weights during the calculation of neighborhoods can make existing methods ineffective. Specifically, a driver intelligence-biased weighting scheme is introduced to estimate the importance of each contextual factor that utilizes taxi drivers' intelligent moves. We argue that the quality of the identified neighborhood is significantly improved by considering the relevant heterogeneous contextual factors, thus boosting the prediction performance (i.e., mean prediction error < 0.09 and median prediction error < 0.06). To support our claim, we generated an airport taxi wait time dataset for the John F. Kennedy International Airport by fusing three real-world contextual datasets, including taxi trip logs, passenger wait times, and weather conditions. Our experimental results demonstrate that the presence of heterogeneous contextual features and the drivers' intelligence-biased weighting scheme significantly outperform the baseline approaches for predicting taxi driver queue wait times.
Subject Neural, Evolutionary and Fuzzy Computation
Computer-Human Interaction
Interorganisational Information Systems and Web Services
Keyword(s) Heterogeneous contextual features
neighborhood identification
wait time prediction
feature weighting
DOI - identifier 10.1109/ACCESS.2018.2882580
Copyright notice © 2018 IEEE
ISSN 2169-3536
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