Passenger flow prediction in bus transportation system using ARIMA models with big data

Yinna Ye; Li Chen; Feng Xue

doi:10.1109/CyberC.2019.00081

Passenger flow prediction in bus transportation system using ARIMA models with big data

Yinna Ye, Li Chen, Feng Xue

Department of Applied Mathematics

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

16 Citations (Scopus)

Abstract

The objective of this research is to predict the daily bus passenger flow volume in a given bus line and compare the prediction performances in the case using whole weekday data against the case using weekday-only data. Based on the real data collected from the bus IC card payment devices in Jiaozuo City, we firstly obtained time series plots on the daily passenger volume and then proposed ARIMA models to do the prediction. The results show that the the operation of including weekend data is necessary to improve the prediction performance.

Original language	English
Title of host publication	Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	436-443
Number of pages	8
ISBN (Electronic)	9781728125411
DOIs	https://doi.org/10.1109/CyberC.2019.00081
Publication status	Published - Oct 2019
Event	2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019 - Guilin, China Duration: 17 Oct 2019 → 19 Oct 2019

Publication series

Name	Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019

Conference

Conference	2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019
Country/Territory	China
City	Guilin
Period	17/10/19 → 19/10/19

Keywords

ARIMA model
ARMA model
Bus transportation system
Passenger flow volume prediction
Time series analysis

Access to Document

10.1109/CyberC.2019.00081

Cite this

Ye, Y., Chen, L., & Xue, F. (2019). Passenger flow prediction in bus transportation system using ARIMA models with big data. In Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019 (pp. 436-443). Article 8945914 (Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CyberC.2019.00081

Ye, Yinna ; Chen, Li ; Xue, Feng. / Passenger flow prediction in bus transportation system using ARIMA models with big data. Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 436-443 (Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019).

@inproceedings{e1fbed102b6947a0a44600585bc70858,

title = "Passenger flow prediction in bus transportation system using ARIMA models with big data",

abstract = "The objective of this research is to predict the daily bus passenger flow volume in a given bus line and compare the prediction performances in the case using whole weekday data against the case using weekday-only data. Based on the real data collected from the bus IC card payment devices in Jiaozuo City, we firstly obtained time series plots on the daily passenger volume and then proposed ARIMA models to do the prediction. The results show that the the operation of including weekend data is necessary to improve the prediction performance.",

keywords = "ARIMA model, ARMA model, Bus transportation system, Passenger flow volume prediction, Time series analysis",

author = "Yinna Ye and Li Chen and Feng Xue",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019 ; Conference date: 17-10-2019 Through 19-10-2019",

year = "2019",

month = oct,

doi = "10.1109/CyberC.2019.00081",

language = "English",

series = "Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "436--443",

booktitle = "Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019",

}

Ye, Y, Chen, L & Xue, F 2019, Passenger flow prediction in bus transportation system using ARIMA models with big data. in Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019., 8945914, Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 436-443, 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019, Guilin, China, 17/10/19. https://doi.org/10.1109/CyberC.2019.00081

Passenger flow prediction in bus transportation system using ARIMA models with big data. / Ye, Yinna; Chen, Li; Xue, Feng.
Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 436-443 8945914 (Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019).

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

TY - GEN

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AU - Ye, Yinna

AU - Chen, Li

AU - Xue, Feng

PY - 2019/10

Y1 - 2019/10

N2 - The objective of this research is to predict the daily bus passenger flow volume in a given bus line and compare the prediction performances in the case using whole weekday data against the case using weekday-only data. Based on the real data collected from the bus IC card payment devices in Jiaozuo City, we firstly obtained time series plots on the daily passenger volume and then proposed ARIMA models to do the prediction. The results show that the the operation of including weekend data is necessary to improve the prediction performance.

AB - The objective of this research is to predict the daily bus passenger flow volume in a given bus line and compare the prediction performances in the case using whole weekday data against the case using weekday-only data. Based on the real data collected from the bus IC card payment devices in Jiaozuo City, we firstly obtained time series plots on the daily passenger volume and then proposed ARIMA models to do the prediction. The results show that the the operation of including weekend data is necessary to improve the prediction performance.

KW - ARIMA model

KW - ARMA model

KW - Bus transportation system

KW - Passenger flow volume prediction

KW - Time series analysis

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DO - 10.1109/CyberC.2019.00081

M3 - Conference Proceeding

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T3 - Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019

SP - 436

EP - 443

BT - Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019

Y2 - 17 October 2019 through 19 October 2019

ER -

Ye Y, Chen L, Xue F. Passenger flow prediction in bus transportation system using ARIMA models with big data. In Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 436-443. 8945914. (Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019). doi: 10.1109/CyberC.2019.00081

Passenger flow prediction in bus transportation system using ARIMA models with big data

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Keywords

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