Applied Decision Analytics

Vol. 2 No. 1 (2026): Applied Decision Analytics
Articles

Business Analytics for Ride-Hailing Platforms: Demand Forecasting and Ride-Completion Modeling

Full text (pdf)
  • Sara Debeljević,
  • Filip Peovski
Sara Debeljević
Faculty of Organizational Sciences, University of Belgrade, Serbia
Filip Peovski
Department of Mathematics and Statistics, Faculty of Economics – Skopje, Ss. Cyril and Methodius University in Skopje, Republic of North Macedonia

Published 2026-04-22

Keywords

  • Business Analytics,
  • User Behavior,
  • Ride-hailing platforms,
  • Demand Forecasting,
  • Service Quality

Copyright (c) 2026 Sara Debeljević, Filip Peovski (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Debeljević, S., & Peovski, F. (2026). Business Analytics for Ride-Hailing Platforms: Demand Forecasting and Ride-Completion Modeling. Applied Decision Analytics, 2(1), 283-294. https://doi.org/10.66972/ada21202625
Crossref
Scopus
Google Scholar
Europe PMC

Abstract

Digital ride-hailing platforms operate in dynamic environments characterized by fluctuating demand and variable user behavior. Business analytics plays a key role in supporting data-driven decision-making and operational planning in such systems. This study examines the application of descriptive and predictive analytics to analyze ride demand and ride completion behavior on a digital transportation platform. The empirical analysis is based on a dataset containing ride-level and user-related information. Descriptive analytics is used to preprocess the data, generate summary statistics, and identify temporal demand patterns. Predictive analytics is applied to forecast short-term ride demand using time-series methods and to model ride completion as a binary outcome using logistic regression. The results indicate that ride demand exhibits recurring temporal patterns suitable for short-horizon forecasting, with exponential smoothing achieving improved accuracy compared to a naïve approach. In addition, waiting time is identified as a key factor influencing ride completion probability. The findings demonstrate that business analytics can support proactive demand management, improved resource allocation, and enhanced understanding of user behavior in ride-hailing services.

Full text (pdf)

Downloads

Download data is not yet available.

References

  1. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  2. Cachon, G. P., Daniels, K. M., & Lobel, R. (2017). The role of surge pricing on a service platform with self-scheduling capacity. Manufacturing & Service Operations Management, 19(3), 368–384. https://doi.org/10.1287/msom.2017.0623
  3. Castillo, J. C., Knoepfle, D., & Weyl, E. G. (2024). Matching and pricing in ride-hailing: Wild goose chases and how to solve them. Management Science, 71(5), 4377–4395. https://doi.org/10.1287/mnsc.2022.00096
  4. Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Cengage Learning.
  5. Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
  6. Liu, S. (2023). A review on business analytics: Definitions, techniques, applications and challenges. Mathematics, 11(4), 899. https://doi.org/10.3390/math11040899
  7. Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: A managerial perspective (4th ed.). Pearson.
  8. Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2–12. https://doi.org/10.1080/2573234X.2018.1507324
  9. Varsha, P. S., & Karan, A. (2023). Descriptive analytics and data visualization in e-commerce. In S. Akter & S. F. Wamba (Eds.), Handbook of Big Data Research Methods (pp. 86–104). Edward Elgar Publishing. https://doi.org/10.4337/9781800373713.00012
  10. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
  11. Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015
  12. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  13. Gardner, E. S. (2006). Exponential smoothing: The state of the art – Part II. International Journal of Forecasting, 22(4), 637–666. https://doi.org/10.1016/j.ijforecast.2006.03.005
  14. Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications (3rd ed.). John Wiley & Sons.
  15. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley & Sons. https://doi.org/10.1002/9781118548387
  16. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). John Wiley & Sons. https://doi.org/10.1002/9780470542811
  17. Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572. https://doi.org/10.2307/23042796
  18. Delen, D., & Demirkan, H. (2013). Data, information and analytics as services. Decision Support Systems, 55(1), 359–363. https://doi.org/10.1016/j.dss.2012.05.044
  19. Waller, M. A., & Fawcett, S. E. (2013). Click here for a data scientist. Journal of Business Logistics, 34(2), 249–252. https://doi.org/10.1111/jbl.12024
  20. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). John Wiley & Sons. https://doi.org/10.1002/9781118619193
  21. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
  22. Wolniak, R. (2023). The applications of usage of business analytics in Industry 4.0. Scientific Papers of Silesian University of Technology, Organization and Management Series, 179, 730–741. https://doi.org/10.29119/1641-3466.2023.179.38