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

Yinna Ye, Li Chen, Feng Xue

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

15 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 languageEnglish
Title of host publicationProceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages436-443
Number of pages8
ISBN (Electronic)9781728125411
DOIs
Publication statusPublished - Oct 2019
Event2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019 - Guilin, China
Duration: 17 Oct 201919 Oct 2019

Publication series

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

Conference

Conference2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019
Country/TerritoryChina
CityGuilin
Period17/10/1919/10/19

Keywords

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

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