IDEAS home Printed from https://ideas.repec.org/a/wly/jnddns/v2016y2016i1n9527584.html

Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network

Author

Listed:
  • Junfang Li
  • Minfeng Yao
  • Qian Fu

Abstract

Direct forecasting method for Urban Rail Transit (URT) ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN) model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership. Key predictors are obtained through partial correlation analysis. The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs), maximum relative error (MRE), smallest relative error (SRE), average relative error (ARE), and mean square root of relative error (MSRRE). Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed. Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value. The result confirms the effectiveness of models proposed in this paper.

Suggested Citation

  • Junfang Li & Minfeng Yao & Qian Fu, 2016. "Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network," Discrete Dynamics in Nature and Society, John Wiley & Sons, vol. 2016(1).
    • Handle: RePEc:wly:jnddns:v:2016:y:2016:i:1:n:9527584
    DOI: 10.1155/2016/9527584
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2016/9527584
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2016/9527584?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kevin Manaugh & Tyler Kreider, 2013. "What is mixed use? Presenting an interaction method for measuring land use mix," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(1), pages 63-72.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paköz, Muhammed Ziya & Yaratgan, Dilara & Şahin, Aydan, 2022. "Re-mapping urban vitality through Jane Jacobs’ criteria: The case of Kayseri, Turkey," Land Use Policy, Elsevier, vol. 114(C).
    2. Letizia Appolloni & Alberto Giretti & Maria Vittoria Corazza & Daniela D’Alessandro, 2020. "Walkable Urban Environments: An Ergonomic Approach of Evaluation," Sustainability, MDPI, vol. 12(20), pages 1-31, October.
    3. Muhammad Mashhood Arif & Muhammad Ahsan & Oswald Devisch & Yves Schoonjans, 2022. "Integrated Approach to Explore Multidimensional Urban Morphology of Informal Settlements: The Case Studies of Lahore, Pakistan," Sustainability, MDPI, vol. 14(13), pages 1-28, June.
    4. Maria Vittoria Corazza & Nicola Favaretto, 2019. "A Methodology to Evaluate Accessibility to Bus Stops as a Contribution to Improve Sustainability in Urban Mobility," Sustainability, MDPI, vol. 11(3), pages 1-23, February.
    5. Ha Na Im & Chang Gyu Choi, 2019. "The hidden side of the entropy-based land-use mix index: Clarifying the relationship between pedestrian volume and land-use mix," Urban Studies, Urban Studies Journal Limited, vol. 56(9), pages 1865-1881, July.
    6. Li, Jianyi & Huang, Hao, 2020. "Effects of transit-oriented development (TOD) on housing prices: A case study in Wuhan, China," Research in Transportation Economics, Elsevier, vol. 80(C).
    7. Hongyu Zheng & Yuefei Zhuo & Zhongguo Xu & Cifang Wu & Jianhong Huang & Qi Fu, 2021. "Measuring and characterizing land use mix patterns of China’s megacities: A case study of Shanghai," Growth and Change, Wiley Blackwell, vol. 52(4), pages 2509-2539, December.
    8. Qu, Boting & Li, Fen & Gao, Yuan, 2026. "Decoding built environment impacts and regional interdependence of urban traffic emissions using interpretable deep learning," Journal of Transport Geography, Elsevier, vol. 130(C).
    9. Yuning Wang & Duanfang Lu & David Levinson, 2023. "Equilibrium or imbalance? Rail Transit and Land Use Mix in Station Areas," Transportation, Springer, vol. 50(6), pages 2403-2421, December.
    10. Dawid Kudas & Agnieszka Wnęk & Lucia Tátošová, 2022. "Land Use Mix in Functional Urban Areas of Selected Central European Countries from 2006 to 2012," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    11. Liu, Sihan & Niu, Xinyi & Cao, Zhan & De Vos, Jonas, 2025. "Assessing the impact of urban place irreplaceability on intercity travel mode choice," Journal of Transport Geography, Elsevier, vol. 127(C).
    12. George Grekousis & Zhuolin Pan & Ye Liu, 2021. "Do Neighborhoods with Highly Diverse Built Environment Exhibit Different Socio-Economic Profiles as Well? Evidence from Shanghai," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    13. Xucai Zhang & Yeran Sun & Ting On Chan & Ying Huang & Anyao Zheng & Zhang Liu, 2021. "Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou," Sustainability, MDPI, vol. 13(2), pages 1-23, January.
    14. Motieyan, Hamid & Azmoodeh, Mohammad, 2021. "Mixed-use distribution index: A novel bilevel measure to address urban land-use mix pattern (A case study in Tehran, Iran)," Land Use Policy, Elsevier, vol. 109(C).
    15. Jiacheng Jiao & John Rollo & Baibai Fu, 2021. "The Hidden Characteristics of Land-Use Mix Indices: An Overview and Validity Analysis Based on the Land Use in Melbourne, Australia," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    16. Chen, Zhenhua & Zhou, Yulong & Haynes, Kingsley E., 2021. "Change in land use structure in urban China: Does the development of high-speed rail make a difference," Land Use Policy, Elsevier, vol. 111(C).
    17. Gehrke, Steven R. & Wang, Liming, 2020. "Operationalizing the neighborhood effects of the built environment on travel behavior," Journal of Transport Geography, Elsevier, vol. 82(C).
    18. Ramezani, Samira & Hasanzadeh, Kamyar & Rinne, Tiina & Kajosaari, Anna & Kyttä, Marketta, 2021. "Residential relocation and travel behavior change: Investigating the effects of changes in the built environment, activity space dispersion, car and bike ownership, and travel attitudes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 28-48.
    19. Lijing Dong & Lingyu Zhang, 2022. "Spatial Coupling Coordination Evaluation of Mixed Land Use and Urban Vitality in Major Cities in China," IJERPH, MDPI, vol. 19(23), pages 1-21, November.
    20. Chengyu Li & Mark Wang & Jianghao Wang & Wenjie Wu, 2016. "The Geography of City Liveliness and Land Use Configurations: Evidence from Location-Based Big Data in Beijing," SERC Discussion Papers 0201, Centre for Economic Performance, LSE.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jnddns:v:2016:y:2016:i:1:n:9527584. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/3059 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.