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

HAN, Dong Hun; JUNG, Sang Woo

doi:10.24225/kjai.2022.10.1.27

[국내논문] A Study on Predicting the demand for Public Shared Bikes using linear Regression 원문보기

Korean Journal of Artificial Intelligence = 인공지능연구, v.10 no.1, 2022년, pp.27 - 32

HAN, Dong Hun (MIIC, Eulji Univ.) , JUNG, Sang Woo (All for Land)

Abstract ▼ AI-Helper

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maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time.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.

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표/그림 (6)

그림 Figure 1: Seoul bike demand data
그림 Figure 2: Azure ML flow
그림 Figure 3: Score model
그림 Figure 4: Data visualization pseudo-code
표 Table 1: Example of Data Set
그림 Figure 5: Data visualization

참고문헌 (28)

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.
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.
Barnes, J. (2015). Azure machine learning. Microsoft Azure Essentials(1st ed), Washington, USA: Microsoft.
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.

상세보기
Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In Machine learning techniques for multimedia. 21-49. Heidelberg, Berlin: Springer.
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.
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.
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

상세보기
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
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.
Douglas C. Montgomery, Elizabeth A. Peck, G. & Geoffrey Vinning (2012). Introduction to Linear Regression Analysis (sixth edition). New York, USA: Wiley.
El Naqa, I., & Murphy, M. J. (2015). What is machine learning?. In machine learning in radiation oncology. 3-11. Springer, Cham.
IBM(2020). Linear regression. Retrieved from https://www.ibm.com/topics/linear-regression
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

상세보기
Kang, M., & Choi, E. (2021). MACHINE LEARNING: Concepts, Tools and Data Visualization. London, En: World Scientific.
Khushbu K., Suniti Y. (2018). Linear regression analysis study. CURRICULUM IN CARDIOLOGY - STATISTICS. 4(1), 33-38. Doi: 10.4103/jpcs.jpcs_8_18

상세보기
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.

상세보기
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.
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

원문보기 상세보기
Liu, B. (2011). Supervised learning. In Web data mining. 63-132. Heidelberg, Berlin: Springer.
Mahesh, B. (2018). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). 9, 381-386.
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.
Microsoft (2021). Azure Machine Learning. Retrieved from https://azure.microsoft.com/ko-kr/services/machine-learning/
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. Cambridge U.S.A.: MIT press.
Paluszek, M., & Thomas, S. (2016). MATLAB. Boston, USA: machine learning Apress.
Shwartz, S. S. (2021). Understanding Machine Learning: From Theory to Algorithms. Cambridge U.S.A.: Cambridge university press.
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.

상세보기
Wikipedia (2021). Linear regression. Retrieved from May 30, 2022. https://en.wikipedia.org/wiki/Linear_regression

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