Prediction for bike-sharing demand in London using multiple linear regression and random forest

Keyu Gu; Yi Lin

doi:10.1117/12.3009514

16 October 2023 Prediction for bike-sharing demand in London using multiple linear regression and random forest

Keyu Gu, Yi Lin

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031I (2023) https://doi.org/10.1117/12.3009514
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

The bike-sharing scheme has become extremely prevalent in large countries around the world. The appearance of it raises a lot of advantages such as reducing greenhouse gas emissions, alleviating traffic congestion, especially implicitly increasing exercise and enhancing health. However, the management of shared bicycles scattered everywhere in the city has become a serious problem. Placing enough bicycles at a certain time in high-demand places can maximize the utilization of bicycles and improve the convenience of people. So the forecast for bike-sharing demand is quite necessary to improve the distribution of bicycles which ensures enough bicycles for the public all the time. In this paper, we study the prediction of bike-sharing demand in London using multiple linear regression and random forest methods based on historical rental bicycle data. We analyze the descriptive statistics and conduct feature engineering using rich relevant factors. The experimental results demonstrate that the random forest model achieves a superb performance with an R-squared value of 0.95 on the test set. This research can be applied in bicycle management to increase bicycle utilization and improve convenience.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Keyu Gu and Yi Lin "Prediction for bike-sharing demand in London using multiple linear regression and random forest", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031I (16 October 2023); https://doi.org/10.1117/12.3009514

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