Regression on Seoul Bike Sharing Demand

DOI

10.2991/978-94-6463-823-3_21How to use a DOI?

Keywords

Supervised Learning; XGBoost; Random Forest; Regression; Prediction

Abstract

A program of shared bikes has been implemented in Seoul by the government as a measure to cut down emissions. The demand for sharing bicycles also surges over time, from 9395 in 1/12/2017 to 16297 in 30/11/2018. In this paper, multiple models of machine learning will be implemented, including Linear Regression, Random Forest Regression, Extreme Gradient Boosting (XGBoost), and others, to fit the dataset and find which feature influences the result most significantly. After comparing the R-squared and mean square error (MSE) of each model, XGBoost has the best performance. And the importance of each feature in different models has been analyzed to show the most significant one. The result of the regression shows that time and temperature share the highest coefficient. The result allows the sharing bike operator to predict the demand more efficiently and accurately and optimize the allocation of human resources and the bikes to maximize efficiency and profit. It will also contribute to solving the excessive budget spending and deficit problems, which have been revealed recently.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiaqi Guo
PY  - 2025
DA  - 2025/08/31
TI  - Regression on Seoul Bike Sharing Demand
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 227
EP  - 235
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_21
DO  - 10.2991/978-94-6463-823-3_21
ID  - Guo2025
ER  -