IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i21p6173-d283743.html
   My bibliography  Save this article

The Predictability of Short-Term Urban Rail Demand: Choice of Time Resolution and Methodology

Author

Listed:
  • Zi-jia Wang

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Hai-xu Liu

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Shi Qiu

    (School of Civil Engineering, Central South University, No. 22 Shaoshan South Road, Changsha 410075, China)

  • Ji-ping Fang

    (Centre for Transport Studies, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E6BT, UK)

  • Ting Wang

    (China Railway First Survey and Design Institute Group Co., Ltd., Xi’an 710073, China)

Abstract

The accuracy of short-term demand forecasting is critical for real-time operation management of urban rail transit, which largely depends on the choice of time resolution. Although there have been continuous improvements in forecasting models, the basic issue has not been well addressed. In this regard, the predictability of short-term demand in terms of time resolution setting and the corresponding model selection have been addressed in this study. Two methods have been considered: the demand forecasting with the past demand during the same time slot on the same weekday (the same period method); and that with continuous time series demand exactly before the forecasted time slot (time series method). The predictability for these two methods was respectively measured by the similarity of the same period and the stability of the time series. Consequently, the influence of time resolution on the predictability of short-term demand for urban rail transit has been evaluated. With the methods proposed, this study conducted an analysis on five-week smartcard data in the Beijing subway system. Results suggest that the predictability of short-term demand presented remarkable heterogeneity in both time and space. The predictability of demand forecasting at station level has been summarized into different levels, and the corresponding methods can be selected for each class. Generally, to ensure a desirable accuracy, forecasting can be made at a 10-min and 60-min interval on weekdays and weekends, respectively. The same period method works better for the short-term demand forecasting on weekdays. While the time series method performs better for prediction on weekends. As for short-time OD (origin-destination) demand, the time series method with a 10-min interval, which is supplemented by the same period method, can generate acceptable forecasting results. In brief, this study provides suggestions on the time resolution and method selection for short-term demand forecasting.

Suggested Citation

  • Zi-jia Wang & Hai-xu Liu & Shi Qiu & Ji-ping Fang & Ting Wang, 2019. "The Predictability of Short-Term Urban Rail Demand: Choice of Time Resolution and Methodology," Sustainability, MDPI, vol. 11(21), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:6173-:d:283743
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/21/6173/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/21/6173/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hauke Jan & Kossowski Tomasz, 2011. "Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data," Quaestiones Geographicae, Sciendo, vol. 30(2), pages 87-93, June.
    2. 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.
    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. Agumas Alamirew Mebratu, 2024. "Theoretical foundations of voluntary tax compliance: evidence from a developing country," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-8, December.
    2. 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.
    3. Alex Bara & Pierre LeRoux, 2018. "Technology, Financial Innovations and Bank Behavior in a Low Income Country," Journal of Economics and Behavioral Studies, AMH International, vol. 10(4), pages 221-234.
    4. Javier García López & Raffaele Sisto & Javier Benayas & Álvaro de Juanes & Julio Lumbreras & Carlos Mataix, 2021. "Assessment of the Results and Methodology of the Sustainable Development Index for Spanish Cities," Sustainability, MDPI, vol. 13(11), pages 1-29, June.
    5. Pan, Yue & Ou, Shenwei & Zhang, Limao & Zhang, Wenjing & Wu, Xianguo & Li, Heng, 2019. "Modeling risks in dependent systems: A Copula-Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 416-431.
    6. Adriana Gómez-Cabrera & Amalia Sanz-Benlloch & Laura Montalban-Domingo & Jose Luis Ponz-Tienda & Eugenio Pellicer, 2020. "Identification of Factors Affecting the Performance of Rural Road Projects in Colombia," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    7. Bouchra Zellou & Hassane Rahali, 2017. "Assessment of reduced-complexity landscape evolution model suitability to adequately simulate flood events in complex flow conditions," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 1-29, March.
    8. Judit Bar-Ilan & Mark Levene, 2015. "The hw-rank: an h-index variant for ranking web pages," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2247-2253, March.
    9. Patrik Silva & Lin Li, 2020. "Urban Crime Occurrences in Association with Built Environment Characteristics: An African Case with Implications for Urban Design," Sustainability, MDPI, vol. 12(7), pages 1-23, April.
    10. Ma Zhong & Rong Xu & Xinyi Liao & Shuangli Zhang, 2019. "Do CSR Ratings Converge in China? A Comparison Between RKS and Hexun Scores," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    11. Sun, Long Long & Hu, Ya Peng & Zhu, Chen Ping, 2023. "Scaling invariance in domestic passenger flight delays in the United States," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    12. Loredana Antronico & Roberto Coscarelli & Francesco De Pascale & Dante Di Matteo, 2020. "Climate Change and Social Perception: A Case Study in Southern Italy," Sustainability, MDPI, vol. 12(17), pages 1-24, August.
    13. Avinash Srikanta Murthy & Norhafiz Azis & Salem Al-Ameri & Mohd Fairouz Mohd Yousof & Jasronita Jasni & Mohd Aizam Talib, 2018. "Investigation of the Effect of Winding Clamping Structure on Frequency Response Signature of 11 kV Distribution Transformer," Energies, MDPI, vol. 11(9), pages 1-13, September.
    14. Upton, Joanna & Constenla-Villoslada, Susana & Barrett, Christopher B., 2022. "Caveat utilitor: A comparative assessment of resilience measurement approaches," Journal of Development Economics, Elsevier, vol. 157(C).
    15. Ishan Goel & Sukant Khurana, 2018. "A Bayesian measure of association that utilizes the underlying distributions of noise and information," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-21, August.
    16. Mariia Kostetckaia & Markus Hametner, 2022. "How Sustainable Development Goals interlinkages influence European Union countries’ progress towards the 2030 Agenda," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 916-926, October.
    17. Byung H. Lee, 2018. "Explaining Cyber Deviance among School-Aged Youth," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 11(2), pages 563-584, April.
    18. Fang Yang & Chunyan Shuai & Qian Qian & Wencong Wang & Mingwei He & Min He & Jaeyoung Lee, 2023. "Predictability of short-term passengers’ origin and destination demands in urban rail transit," Transportation, Springer, vol. 50(6), pages 2375-2401, December.
    19. Severini, Simone & Tantari, Antonella & Di Tommaso, Giuliano, 2016. "The instability of farm income. Empirical evidences on aggregation bias and heterogeneity among farm groups," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 5(1), pages 1-19, April.
    20. Chen, Chundi & Wang, Yuncai & Jia, Junsong & Mao, Longfei & Meurk, Colin D., 2019. "Ecosystem services mapping in practice: A Pasteur’s quadrant perspective," Ecosystem Services, Elsevier, vol. 40(C).

    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:gam:jsusta:v:11:y:2019:i:21:p:6173-:d:283743. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.