Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data

Yinna Ye; Ruoxi Liu; Feng Xue

doi:10.1007/978-981-15-4917-5_36

Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data

Yinna Ye^*, Ruoxi Liu, Feng Xue

^*Corresponding author for this work

Department of Applied Mathematics

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

2 Citations (Scopus)

Abstract

Based on the real data collected from the bus IC card payment devices, first a time series plot on the daily passenger volume was obtained and then three kinds of time series models were proposed to do the prediction. The results show that the ARMA model with quadratic trend is the most suitable to the current data and performs the most effectively in the prediction.

Original language	English
Title of host publication	Sensor Networks and Signal Processing - Proceedings of the 2nd Sensor Networks and Signal Processing SNSP 2019
Editors	Sheng-Lung Peng, Margarita N. Favorskaya, Han-Chieh Chao
Publisher	Springer
Pages	497-520
Number of pages	24
ISBN (Print)	9789811549168
DOIs	https://doi.org/10.1007/978-981-15-4917-5_36
Publication status	Published - 2021
Event	2nd International Conference on Sensor Networks and Signal Processing, SNSP 2019 - Hualien, Taiwan, Province of China Duration: 19 Nov 2019 → 22 Nov 2019

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	176
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	2nd International Conference on Sensor Networks and Signal Processing, SNSP 2019
Country/Territory	Taiwan, Province of China
City	Hualien
Period	19/11/19 → 22/11/19

Keywords

ARIMA model
ARMA model
Passenger flow prediction
Quadratic trend
Time series analysis

Access to Document

10.1007/978-981-15-4917-5_36Licence: CC BY-NC-ND

Cite this

Ye, Y., Liu, R., & Xue, F. (2021). Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data. In S.-L. Peng, M. N. Favorskaya, & H.-C. Chao (Eds.), Sensor Networks and Signal Processing - Proceedings of the 2nd Sensor Networks and Signal Processing SNSP 2019 (pp. 497-520). (Smart Innovation, Systems and Technologies; Vol. 176). Springer. https://doi.org/10.1007/978-981-15-4917-5_36

Ye, Yinna ; Liu, Ruoxi ; Xue, Feng. / Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data. Sensor Networks and Signal Processing - Proceedings of the 2nd Sensor Networks and Signal Processing SNSP 2019. editor / Sheng-Lung Peng ; Margarita N. Favorskaya ; Han-Chieh Chao. Springer, 2021. pp. 497-520 (Smart Innovation, Systems and Technologies).

@inproceedings{9895aaa4bc7647a2b915be140e129997,

title = "Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data",

abstract = "Based on the real data collected from the bus IC card payment devices, first a time series plot on the daily passenger volume was obtained and then three kinds of time series models were proposed to do the prediction. The results show that the ARMA model with quadratic trend is the most suitable to the current data and performs the most effectively in the prediction.",

keywords = "ARIMA model, ARMA model, Passenger flow prediction, Quadratic trend, Time series analysis",

author = "Yinna Ye and Ruoxi Liu and Feng Xue",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd 2021.; 2nd International Conference on Sensor Networks and Signal Processing, SNSP 2019 ; Conference date: 19-11-2019 Through 22-11-2019",

year = "2021",

doi = "10.1007/978-981-15-4917-5_36",

language = "English",

isbn = "9789811549168",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer",

pages = "497--520",

editor = "Sheng-Lung Peng and Favorskaya, {Margarita N.} and Han-Chieh Chao",

booktitle = "Sensor Networks and Signal Processing - Proceedings of the 2nd Sensor Networks and Signal Processing SNSP 2019",

}

Ye, Y, Liu, R & Xue, F 2021, Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data. in S-L Peng, MN Favorskaya & H-C Chao (eds), Sensor Networks and Signal Processing - Proceedings of the 2nd Sensor Networks and Signal Processing SNSP 2019. Smart Innovation, Systems and Technologies, vol. 176, Springer, pp. 497-520, 2nd International Conference on Sensor Networks and Signal Processing, SNSP 2019, Hualien, Taiwan, Province of China, 19/11/19. https://doi.org/10.1007/978-981-15-4917-5_36

Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data. / Ye, Yinna; Liu, Ruoxi; Xue, Feng.
Sensor Networks and Signal Processing - Proceedings of the 2nd Sensor Networks and Signal Processing SNSP 2019. ed. / Sheng-Lung Peng; Margarita N. Favorskaya; Han-Chieh Chao. Springer, 2021. p. 497-520 (Smart Innovation, Systems and Technologies; Vol. 176).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data

AU - Ye, Yinna

AU - Liu, Ruoxi

AU - Xue, Feng

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd 2021.

PY - 2021

Y1 - 2021

N2 - Based on the real data collected from the bus IC card payment devices, first a time series plot on the daily passenger volume was obtained and then three kinds of time series models were proposed to do the prediction. The results show that the ARMA model with quadratic trend is the most suitable to the current data and performs the most effectively in the prediction.

AB - Based on the real data collected from the bus IC card payment devices, first a time series plot on the daily passenger volume was obtained and then three kinds of time series models were proposed to do the prediction. The results show that the ARMA model with quadratic trend is the most suitable to the current data and performs the most effectively in the prediction.

KW - ARIMA model

KW - ARMA model

KW - Passenger flow prediction

KW - Quadratic trend

KW - Time series analysis

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U2 - 10.1007/978-981-15-4917-5_36

DO - 10.1007/978-981-15-4917-5_36

M3 - Conference Proceeding

AN - SCOPUS:85089226304

SN - 9789811549168

T3 - Smart Innovation, Systems and Technologies

SP - 497

EP - 520

BT - Sensor Networks and Signal Processing - Proceedings of the 2nd Sensor Networks and Signal Processing SNSP 2019

A2 - Peng, Sheng-Lung

A2 - Favorskaya, Margarita N.

A2 - Chao, Han-Chieh

PB - Springer

T2 - 2nd International Conference on Sensor Networks and Signal Processing, SNSP 2019

Y2 - 19 November 2019 through 22 November 2019

ER -

Ye Y, Liu R, Xue F. Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data. In Peng SL, Favorskaya MN, Chao HC, editors, Sensor Networks and Signal Processing - Proceedings of the 2nd Sensor Networks and Signal Processing SNSP 2019. Springer. 2021. p. 497-520. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-15-4917-5_36

Application of time series method to the passenger flow prediction in the intelligent bus transportation system with big data

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