Sensors and Materials

Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 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.

Tweets by Journal_SandM Sensors and Materials
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 11(4) (2020)
Copyright(C) MYU K.K.

pp. 3893-3906
S&M2383 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2969
Published: November 30, 2020

A Multiscale Sequential Data Assimilation System and Its Application to Short-term Traffic Flow Prediction [PDF]

Wenzhong Shi and Runjie Wang

(Received June 28, 2020; Accepted November 4, 2020)

Keywords: sequential data assimilation, assimilation models, historical measurements, multiscale noise separation, short-term traffic flow prediction

We present a multiscale sequential data assimilation (M-SDA) system and apply it to short-term traffic flow prediction. Assimilation models in traditional sequential data assimilation (T-SDA) systems, which are usually constructed using historical measurements, are always disturbed by local noises. Simultaneously, the accuracy of assimilation results is also affected. To reduce the effects of these noises on assimilation models and the accuracy of results, an M-SDA system combining a T-SDA system and noise separation methods is constructed. This paper comprises four main parts: (1) a T-SDA system for short-term traffic flow prediction and multiscale noise separation methods are briefly discussed, and an example of denoised measurements with separated multiscale noises is given; (2) an M-SDA system for short-term traffic flow prediction is established; (3) the impacts of different noise separation scales on the accuracy of assimilation results are analyzed; and (4) applications of the M-SDA system to short-term traffic flow prediction are presented and compared with those of a T-SDA system. Experimental results were acquired from traffic flow measurements collected from a sub-area of a highway near Liverpool and Manchester, UK. The gap between the true and predicted values was evaluated by the root mean square error (RMSE) and mean absolute percent error (MAPE). By comparison with the prediction results from the T-SDA system, it was experimentally shown that the M-SDA system can successfully reduce the effects of noises in historical measurements on assimilation model construction and improve the accuracy of short-term traffic flow prediction results.

Corresponding author: Runjie Wang

This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Wenzhong Shi and Runjie Wang, A Multiscale Sequential Data Assimilation System and Its Application to Short-term Traffic Flow Prediction, Sens. Mater., Vol. 32, No. 11, 2020, p. 3893-3906.