Paper Abstract and Keywords |
Presentation |
2021-12-10 15:25
Prediction of Train Delays at Stations Using Multiple Convolutional Neural Networks with Actual Operation Data Tsukasa Takahashi, Takumi Fukuda, Sei Takahashi (Nihon Univ.), Hideo Nakamura (UTokyo) DC2021-61 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In the metropolitan area, railroads are frequently delayed due to high congestion rates during rush hours, and many measures are being taken to mitigate delays. A train operation simulator has been developed as a means of evaluating the effects of countermeasures, but this simulator evaluates the effects before and after countermeasures by applying known delays. The simulator evaluates the effect before and after the countermeasure by giving a known delay. However, the delay generated by the countermeasure changes according to the change of running conditions, so the generation of station generated delay is essential for the evaluation. In this study, we aimed to improve the prediction accuracy by using a large amount of train operation data to predict the station generated delay. As a result, the highest prediction accuracy of 75.9% was obtained. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Delay resolution / Delay improvement / Train delay / Operation management / Machine learning / Convolutional neural network / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 293, DC2021-61, pp. 34-37, Dec. 2021. |
Paper # |
DC2021-61 |
Date of Issue |
2021-12-03 (DC) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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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DC2021-61 |
Conference Information |
Committee |
DC |
Conference Date |
2021-12-10 - 2021-12-10 |
Place (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
DC |
Conference Code |
2021-12-DC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Prediction of Train Delays at Stations Using Multiple Convolutional Neural Networks with Actual Operation Data |
Sub Title (in English) |
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Keyword(1) |
Delay resolution |
Keyword(2) |
Delay improvement |
Keyword(3) |
Train delay |
Keyword(4) |
Operation management |
Keyword(5) |
Machine learning |
Keyword(6) |
Convolutional neural network |
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1st Author's Name |
Tsukasa Takahashi |
1st Author's Affiliation |
Nihon University (Nihon Univ.) |
2nd Author's Name |
Takumi Fukuda |
2nd Author's Affiliation |
Nihon University (Nihon Univ.) |
3rd Author's Name |
Sei Takahashi |
3rd Author's Affiliation |
Nihon University (Nihon Univ.) |
4th Author's Name |
Hideo Nakamura |
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The University of Tokyo (UTokyo) |
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Speaker |
Author-1 |
Date Time |
2021-12-10 15:25:00 |
Presentation Time |
20 minutes |
Registration for |
DC |
Paper # |
DC2021-61 |
Volume (vol) |
vol.121 |
Number (no) |
no.293 |
Page |
pp.34-37 |
#Pages |
4 |
Date of Issue |
2021-12-03 (DC) |
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