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Deviation of Peak Hours for Urban Rail Transit Stations: A Case Study in Xi’an, China

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  • Lijie Yu

    (Department of Traffic Engineering, Highway School, Chang’an University, Xi’an 710064, China)

  • Quan Chen

    (Department of Traffic Engineering, Highway School, Chang’an University, Xi’an 710064, China)

  • Kuanmin Chen

    (Department of Traffic Engineering, Highway School, Chang’an University, Xi’an 710064, China)

Abstract

The inconsistencies of passenger flow volume between stations’ peak hours and cities’ peak hours have emerged as a phenomenon in various cities worldwide. Passenger flow forecasting at planning stages can only predict passenger flow volume in city peak hours and for the whole day. For some stations, the highest flow does not occur in the city peak hours, and station scale design is often too small. This study locates the formation mechanism of station peak in which the temporal distribution of the station is the superposition of different temporal distributions of the purpose determined by land-use attributes. Data from 63 stations in Xi’an, China, were then used to present an enlargement coefficient which can change the boarding and alighting volume in city peak hours to a station’s own peak hours. This was done by analyzing the inconsistencies of passenger flow volume between the station’s peak hours and the city’s peak hours. Morning peak deviation coefficient (PDC) and evening PDC were selected as datasets, and stations were classified accordingly. Statistics of land usage for every type of station showed that when the stations were surrounded by developed land, the relationship between the PDC and the commuter travel land proportion was to some extent orderly. More than 90.00% of stations with a proportion of commuter travel land that was more than 0.50 had PDCs under 1.10. All stations with a proportion of commuter travel land that was less than 0.50 had morning PDCs over 1.10. Finally, data from 52 stations in Chongqing, China were used to verify the findings, with the results in Chongqing predominantly corresponding to those in Xi’an.

Suggested Citation

  • Lijie Yu & Quan Chen & Kuanmin Chen, 2019. "Deviation of Peak Hours for Urban Rail Transit Stations: A Case Study in Xi’an, China," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2733-:d:230841
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    References listed on IDEAS

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    1. Kuby, Michael & Barranda, Anthony & Upchurch, Christopher, 2004. "Factors influencing light-rail station boardings in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 223-247, March.
    2. Zhuangbin Shi & Ning Zhang & Yang Liu & Wei Xu, 2018. "Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    3. Chen Zhong & Michael Batty & Ed Manley & Jiaqiu Wang & Zijia Wang & Feng Chen & Gerhard Schmitt, 2016. "Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
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    Cited by:

    1. Peikun Li & Chaoqun Ma & Jing Ning & Yun Wang & Caihua Zhu, 2019. "Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    2. Quantao Yang & Feng Lu & Jingsheng Wang & Dan Zhao & Lijie Yu, 2020. "Analysis of the Insertion Angle of Lane-Changing Vehicles in Nearly Saturated Fast Road Segments," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    3. Ying Zhao & Jie Wei & Haijun Li & Yan Huang, 2024. "Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient," Sustainability, MDPI, vol. 16(3), pages 1-16, February.
    4. Lijie Yu & Yarong Cong & Kuanmin Chen, 2020. "Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression," Sustainability, MDPI, vol. 12(6), pages 1-22, March.
    5. Yi Cao & Xue Li, 2022. "Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19," Sustainability, MDPI, vol. 14(21), pages 1-16, November.

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