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Queue context prediction using taxi driver knowledge

conference contribution
posted on 2023-12-12, 03:13 authored by Mohammad Saiedur Rahaman, M Hamilton, FD Salim
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.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

1

End Page

4

Number of Pages

4

Start Date

2017-12-04

Finish Date

2017-12-06

ISBN-13

9781450355537

Location

Austin, USA

Publisher

ACM

Place of Publication

NY, United States

Peer Reviewed

  • Yes

Open Access

  • No

Era Eligible

  • Yes

Name of Conference

9th International Conference on Knowledge Capture (K-CAP 2017)

Parent Title

K-CAP '17: Proceedings of the 9th Knowledge Capture Conference

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