Urban rail transit demand analysis and prediction: A review of recent studies

Zhiyan Fang, Qixiu Cheng, Ruo Jia, Zhiyuan Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Urban rail transit demand analysis and forecasting is an essential prerequisite for daily operations and management. This paper categorizes the proposed demand forecasting methods, and focuses on traditional models, statistical models and machine learning approaches, according to their features and fields. Especially, influential and widely-used methods including the four-stage model, land use models, time series methods, Logit regression, Artificial Neural Networks (ANNs) and other referring methods are all taken into discussion.

Original languageEnglish
Title of host publicationIntelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference
EditorsRobert J. Howlett, Lakhmi C. Jain, Lakhmi C. Jain, Giuseppe De Pietro, Luigi Gallo, Lakhmi C. Jain, Ljubo Vlacic, Robert J. Howlett
PublisherSpringer Science and Business Media Deutschland GmbH
Pages300-309
Number of pages10
ISBN (Print)9783319922300
DOIs
Publication statusPublished - Jun 2018
Externally publishedYes
Event11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018 - Gold Coast, Australia
Duration: 20 Jun 201822 Jun 2018

Publication series

NameSmart Innovation, Systems and Technologies
Volume98
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018
Country/TerritoryAustralia
CityGold Coast
Period20/06/1822/06/18

ASJC Scopus subject areas

  • General Decision Sciences
  • General Computer Science

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