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.


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    Title :

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


    Contributors:
    Zaidi, Habib (editor) / Shmaliy, Yuriy S. (editor) / Meng, Hongying (editor) / Kolivand, Hoshang (editor) / Sun, Yougang (editor) / Luo, Jianping (editor) / Alazab, Mamoun (editor) / Gu, Keyu (author) / Lin, Yi (author)

    Conference:

    Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023) ; 2023 ; Wuhan, China


    Published in:

    Proc. SPIE ; 12803


    Publication date :

    2023-10-16





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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