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Paper Abstract and Keywords
Presentation 2018-07-02 11:15
An investigation of prediction accuracy of bus delay time influenced by the amount of training data and the variation of outlier detection
Tsubasa Yamaguchi, Mansur AS, Tsunenori Mine (Kyushu Univ.) AI2018-4
Abstract (in Japanese) (See Japanese page) 
(in English) Prediction of bus delay is one of crucial research tasks in the intelligent transport systems (ITS) field, and is important not only for a bus operation company, but also passengers, who want to know how many minutes a bus will be concretely delayed to get at a bus stop. In this paper, we propose methods to predict bus delay using bus probe data collected from Nov. 21st to Dec. 20th, 2013 and provided by Nishitetsu Bus company. We used several machine learning methods to build the prediction models. Wediscuss experimental results from the points that how much extents the difference of the amount of training data and the variation of outlier detection methods affect prediction accuracy of predicting bus delay.
Keyword (in Japanese) (See Japanese page) 
(in English) scheduled bus / delay time prediction / probe data / machine learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 116, AI2018-4, pp. 15-21, July 2018.
Paper # AI2018-4 
Date of Issue 2018-06-25 (AI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee AI  
Conference Date 2018-07-02 - 2018-07-02 
Place (in Japanese) (See Japanese page) 
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Paper Information
Registration To AI 
Conference Code 2018-07-AI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An investigation of prediction accuracy of bus delay time influenced by the amount of training data and the variation of outlier detection 
Sub Title (in English)  
Keyword(1) scheduled bus  
Keyword(2) delay time prediction  
Keyword(3) probe data  
Keyword(4) machine learning  
1st Author's Name Tsubasa Yamaguchi  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Mansur AS  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Tsunenori Mine  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
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Date Time 2018-07-02 11:15:00 
Presentation Time 25 
Registration for AI 
Paper # IEICE-AI2018-4 
Volume (vol) IEICE-118 
Number (no) no.116 
Page pp.15-21 
#Pages IEICE-7 
Date of Issue IEICE-AI-2018-06-25 

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