S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

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.
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. 3907-3921
S&M2384 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2970
Published: November 30, 2020

Method of Predicting Passenger Flow in Scenic Areas Considering Multisource Traffic Data [PDF]

Zhiwen Gao, Jianqin Zhang, Zhijie Xu, Xuedong Zhang, Ruixuan Shi, Jiachuan Wang, Ying Ding, and Zhuohang Li

(Received June 29, 2020; Accepted October 20, 2020)

Keywords: scenic area, passenger flow, multisource data, CNN-LSTM, prediction model

The rapid growth of passenger flows has brought a series of challenges to the environment and safety management of tourist attractions. It is vital to establish an accurate passenger flow prediction model to reduce the risks associated with human flows. Owing to the limitation of a single data source, the existing research on the prediction of tourist flows in scenic spots ignores the impact of public transport passengers on the internal tourist flow in scenic areas. The prediction model lacks the learning process of data samples, and the ability of generalization and self-study is weak. In this paper, we propose a new method of predicting passenger flow in scenic areas based on a convolutional neural network and long short-term memory (CNN-LSTM) hybrid neural network (HNN) model, which considers the multisource traffic flow around a scenic area. It uses a series of HNNs to mine the temporal and spatial correlation between the passenger flows from multiple sources and solves the problem of data stability dependence. The time series of the passenger flow in the study area was designed and extracted on the basis of the spatial analysis of South Luogu Lane in Beijing, and the input structure was constructed by combining the multisource traffic passenger flow dimension. This model for predicting passenger flow in scenic areas based on CNN-LSTM provides a reference for the comprehensive application of multisource data in scenic areas and has high accuracy and robustness.

Corresponding author: Jianqin Zhang, Zhijie Xu


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

Cite this article
Zhiwen Gao, Jianqin Zhang, Zhijie Xu, Xuedong Zhang, Ruixuan Shi, Jiachuan Wang, Ying Ding, and Zhuohang Li, Method of Predicting Passenger Flow in Scenic Areas Considering Multisource Traffic Data, Sens. Mater., Vol. 32, No. 11, 2020, p. 3907-3921.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Advanced Materials and Sensing Technologies on IoT Applications: Part 1(1)
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2020 (IMETI2020)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website


Special Issue on Advanced Materials and Sensing Technologies on IoT Applications: Part 1(2)
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper


Special Issue on Artificial Intelligence and Advanced Technologies for Power and Renewable Energy Systems Mainly from IS3C2020
Guest editor, Shiue Der Lu , Meng Hui Wang, Kuei Hsiang Chao, and Her Terng Yau (National Chin-Yi University of Technology)
Conference website
Call for paper


Special Issue on Human-in-the-loop Sensing in Cognitive Robotic Systems
Guest editor, Weiwei Wan (Osaka University), Yiming Jiang (Hunan University), and Daolin Ma (MIT)
Call for paper


Special Issue on Sensing and Data Analysis Technologies for Living Environment, Health Care, Production Management and Engineering/Science Education Applications: Part 2
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Rey-Chue Hwang (I-Shou University), Ja-Hao Chen (Feng Chia University), Ba-Son Nguyen (Research Center for Applied Sciences)
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.