Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Predicting the demand for Public Shared Bikes using linear Regression

  • Received : 2022.05.11
  • Accepted : 2022.06.03
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.

Keywords

Acknowledgement

This paper was supported by the research grant of the KODISA scholarship foundation in 2022

References

  1. Abdulqader D. M., Abdulazeez, A. M., & Zeebaree, D. Q. (2020). Machine Learning Supervised Algorithms of Gene Selection: A Review. Technology Reports of Kansai University, 62(3), 23-27.
  2. Barga, R., Fontama, V., & Tok, W. H. (2015). Introducing microsoft azure machine learning. In Predictive Analytics with Microsoft Azure Machine Learning. pp 21-43. Berkeley, CA: Apress.
  3. Barnes, J. (2015). Azure machine learning. Microsoft Azure Essentials(1st ed), Washington, USA: Microsoft.
  4. Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American journal of epidemiology, 188(12), 2222-2239.
  5. Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In Machine learning techniques for multimedia. 21-49. Heidelberg, Berlin: Springer.
  6. Dehghan, M. H., Hamidi, F., & Salajegheh, M. (2015, September). Study of linear regression based on least squares and fuzzy least absolutes deviations and its application in geography. In 2015 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) (pp. 1-6). IEEE.
  7. Dehghan, M. H., Hamidi, F., & Salajegheh, M. (2015, September). Study of linear regression based on least squares and fuzzy least absolutes deviations and its application in geography. In 2015 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) (pp. 1-6). IEEE.
  8. DeMaio, P. (2009). Bike-sharing: History, Impacts, Models of Provision, and Future. Journal of Public Transportation, 12(4), 41-56. Doi: http://doi.org/10.5038/2375-0901.12.4.3
  9. Di Bucchianico, A. (2008). Coefficient of Determination. The Concise Encyclopedia of Statistics. NY, USA: Springer, Doi: https://doi.org/10.1007/978-0-387-32833-162
  10. Dike, H. U., Zhou, Y., Deveerasetty, K. K., & Wu, Q. (2018, October). Unsupervised learning based on artificial neural network: A review. IEEE International Conference on Cyborg and Bionic Systems (CBS) (pp. 322-327). IEEE.
  11. Douglas C. Montgomery, Elizabeth A. Peck, G. & Geoffrey Vinning (2012). Introduction to Linear Regression Analysis (sixth edition). New York, USA: Wiley.
  12. El Naqa, I., & Murphy, M. J. (2015). What is machine learning?. In machine learning in radiation oncology. 3-11. Springer, Cham.
  13. IBM(2020). Linear regression. Retrieved from https://www.ibm.com/topics/linear-regression
  14. J. Schuijbroek, R.C. Hampshire, W.-J. van Hoeve (2017). Inventory rebalancing and vehicle routing in bike sharing systems. European Journal of Operational Research, 257(3), 992-1004. Doi: https://doi.org/10.1016/j.ejor.2016.08.029
  15. Kang, M., & Choi, E. (2021). MACHINE LEARNING: Concepts, Tools and Data Visualization. London, En: World Scientific.
  16. Khushbu K., Suniti Y. (2018). Linear regression analysis study. CURRICULUM IN CARDIOLOGY - STATISTICS. 4(1), 33-38. Doi: 10.4103/jpcs.jpcs_8_18
  17. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.005
  18. K. P. Murphy (2012). Machine learning: a probabilistic perspective. Cambridge U.S.A.: MIT press. Doi: https://doi.org/10.1016/j.csbj.2014.11.005.
  19. Lim H. J., Jung K. H. (2019). Development of Demand Forecasting Model for Seoul Shared Bicycle. The Journal of the Korea Contents Association, 19(1), 132-140. Doi: https://doi.org/10.5392/JKCA.2019.19.01.132
  20. Liu, B. (2011). Supervised learning. In Web data mining. 63-132. Heidelberg, Berlin: Springer.
  21. Mahesh, B. (2018). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). 9, 381-386.
  22. Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(4), 140-147. https://doi.org/10.38094/jastt1457
  23. Microsoft (2021). Azure Machine Learning. Retrieved from https://azure.microsoft.com/ko-kr/services/machine-learning/
  24. Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. Cambridge U.S.A.: MIT press.
  25. Paluszek, M., & Thomas, S. (2016). MATLAB. Boston, USA: machine learning Apress.
  26. Shwartz, S. S. (2021). Understanding Machine Learning: From Theory to Algorithms. Cambridge U.S.A.: Cambridge university press.
  27. Raviv, T., Tzur, M., Forma, I. A. (2013). Static repositioning in a bike-sharing system: models and solution approaches. EURO Journal on Transportation and Logistics. 2(3), 187-229. Doi: https://doi.org/10.1007/s13676-012-0017-6.
  28. Wikipedia (2021). Linear regression. Retrieved from May 30, 2022. https://en.wikipedia.org/wiki/Linear_regression