Queue Context Prediction Using Taxi Driver Knowledge

Saiedur Rahaman, M, Hamilton, M and Salim, F 2017, 'Queue Context Prediction Using Taxi Driver Knowledge', in Proceedings of the Knowledge Capture Conference, Austin, United States, 4-6 December 2017, pp. 1-4.

Document type: Conference Paper
Collection: Conference Papers

Title Queue Context Prediction Using Taxi Driver Knowledge
Author(s) Saiedur Rahaman, M
Hamilton, M
Salim, F
Year 2017
Conference name K-CAP 2017: Knowledge Capture Conference
Conference location Austin, United States
Conference dates 4-6 December 2017
Proceedings title Proceedings of the Knowledge Capture Conference
Publisher ACM Digital Library
Place of publication New York, United States
Start page 1
End page 4
Total pages 4
Abstract 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.
Subjects Computer-Human Interaction
Global Information Systems
Keyword(s) Temporal driver-knowledge deviation
neighborhood selection
queue context prediction
DOI - identifier 10.1145/3148011.3154474
Copyright notice © 2017 Association for Computing Machinery
ISBN 9781450355537
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