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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 4441-4447
S&M2420 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.3111 Published: December 29, 2020 Bus Travel Speed Prediction Using Long Short-term Memory Neural Network [PDF] Seung-Bae Jeon, Myeong-Hun Jeong, Tae-Young Lee, Jeong-Hwan Lee, and Jae-Myoung Cho (Received September 22, 2020; Accepted December 1, 2020) Keywords: long short-term memory neural network, bus travel speed prediction, digital tachograph, autoregressive integrated moving average
Improving the accuracy of public transport information has attracted attention in the development of smart cities. We aim to predict the bus travel speed on road sections using a long short-term memory (LSTM) neural network. We use digital tachograph (DTG) data combined with road link data. Motion sensors in DTG can record vehicle’s operation information, such as journey distance, speed, and driving time. The experimental results show that the proposed model based on LSTM performs better than the autoregressive integrated moving average (ARIMA) model. The accuracy was improved by 20% on average.
Corresponding author: Myeong-Hun Jeong  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Seung-Bae Jeon, Myeong-Hun Jeong, Tae-Young Lee, Jeong-Hwan Lee, and Jae-Myoung Cho, Bus Travel Speed Prediction Using Long Short-term Memory Neural Network, Sens. Mater., Vol. 32, No. 12, 2020, p. 4441-4447. |
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