This paper addresses the problem of taxi-passenger queue context prediction using neighborhood based methods. We capture the taxi drivers’ knowledge based on how they move in terms of temporal driver-knowledge deviation (TDKD). Then a TDKD-aided feature importance scheme is introduced for neighborhood based queue context prediction. We apply our proposed scheme to predict different queue contexts at a busy international airport in New York. We argue that the incorporation of taxi drivers’ knowledge for calculating feature importance significantly improves the quality of selected neighborhood, thus boosting the prediction accuracy. The experimental results demonstrate the effectiveness of our proposed TDKD-aided feature importance scheme for neighborhood based taxi-passenger queue context prediction.
@inproceedings{DBLP:conf/kcap/RahamanHS17,
author = {Mohammad Saiedur Rahaman and
Margaret Hamilton and
Flora D. Salim},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/kcap/RahamanHS17.bib},
booktitle = {Proceedings of the Knowledge Capture Conference, K-CAP 2017, Austin,
TX, USA, December 4-6, 2017},
doi = {10.1145/3148011.3154474},
editor = {Óscar Corcho and
Krzysztof Janowicz and
Giuseppe Rizzo and
Ilaria Tiddi and
Daniel Garijo},
pages = {35:1--35:4},
publisher = {ACM},
timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
title = {Queue Context Prediction Using Taxi Driver Knowledge},
url = {https://doi.org/10.1145/3148011.3154474},
year = {2017}
}
© 2021 Flora Salim - CRUISE Research Group.