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Urban Rail Transit Demand Analysis and Prediction: A Review of Recent Studies

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Intelligent Interactive Multimedia Systems and Services (KES-IIMSS-18 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 98))

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.

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Acknowledgement

This study is supported by the General Projects (No. 71771050) and Key Projects (No. 51638004) of the National Natural Science Foundation of China, and the Natural Science Foundation of Jiangsu Province in China (BK20150603).

Author information

Authors and Affiliations

  1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, China

    Zhiyan Fang, Qixiu Cheng, Ruo Jia & Zhiyuan Liu

Authors
  1. Zhiyan Fang
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  2. Qixiu Cheng
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  3. Ruo Jia
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  4. Zhiyuan Liu
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Corresponding author

Correspondence to Qixiu Cheng .

Editor information

Editors and Affiliations

  1. Istituto Di Calcolo E Reti Ad Alte Prestazioni (Icar), National Research Council, Roma, Italy

    Giuseppe De Pietro

  2. Istituto Di Calcolo E Reti Ad Alte Prestazioni (Icar), National Research Council, Roma, Italy

    Luigi Gallo

  3. Bournemouth University, Poole, United Kingdom

    Robert J. Howlett

  4. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia

    Lakhmi C. Jain

  5. Griffith Sciences - Centres and Institutes, Griffith University, South Brisbane, Queensland, Australia

    Ljubo Vlacic

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Fang, Z., Cheng, Q., Jia, R., Liu, Z. (2019). Urban Rail Transit Demand Analysis and Prediction: A Review of Recent Studies. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L., Vlacic, L. (eds) Intelligent Interactive Multimedia Systems and Services. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-319-92231-7_31

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