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Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway

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  • Hainan Huang

    (Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
    College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Yi Lin

    (National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China)

  • Jiancheng Weng

    (Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Jian Rong

    (Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Xiaoming Liu

    (Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

Abstract

Urban rail transit has become an indispensable option for Beijing residents. Subway inelastic users (SIUs) are the main component among all users. Understanding the proportion of SIUs and their characteristics is important in developing service promotions and helpful for subway agencies in making marketing policies. This paper proposes a novel and simple identification process for identifying regular subway inelastic trips (SITs) in order to distinguish SITs and non-SITs and extract their characteristics. Weekly station sequence (WSS) is selected as the data-based format, principles of SIUs are discussed and chosen, and the framework of SIT identification is applied to a large weekly sample from the Beijing Subway. A revealed preference (RP) survey and results analysis are undertaken to estimate the performance of the proposed methods. The RP survey validation shows that accuracy reaches as high as 94%, and the distribution analysis of SITs and their origin-destinations (ODs) indicate that the SIT characteristics extracted are consistent with the situation in Beijing. The proportion of SIUs is stable on workdays and is more than 80% during rush hour. The efforts described in this paper can provide subway managers with a useful and convenient method to understand the characteristics of subway passengers and the performance of a subway system.

Suggested Citation

  • Hainan Huang & Yi Lin & Jiancheng Weng & Jian Rong & Xiaoming Liu, 2018. "Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4725-:d:189850
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    References listed on IDEAS

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