Intelligent Forecasting of City Bike-Share Trends for Sustainable Urban Transport
Keywords:
Bike Rental, Shared-Mobility, Predictive Analytics, Linear Regression, Random Forest Regression, Gradient Boosting Regression, Demand Prediction.Abstract
Bike-sharing systems play a significant role in promoting sustainable urbanmobility. However, their planning and operation present several challenges. One of theprimary operational issues is the uneven distribution of bicycles across the service area,leading to shortages in some zones and surpluses in others
References
Abouelela, M.; Lyu, C.; Antoniou, C. Exploring the potentials of open-source big data and machine learning in shared mobility fleet utilization prediction. Data Sci. Transp. 2023, 5, 5.
Li,X.;Xu,Y.;Zhang,X.;Shi,W.;Yue,Y.;Li,Q. Improving short-term bike sharing demand forecast through an irregular convolutional neural network.Transp.Res.PartCEmerg.Technol.2023,147,103984.
Downloads
Published
2025-06-30
How to Cite
Mr P. Venkata Siva|Mr G. Bharath Kumar|Mr Sk. John Sydulu|Sk.Hazi Hassain. (2025). Intelligent Forecasting of City Bike-Share Trends for Sustainable Urban Transport . Journal of Computational Analysis and Applications (JoCAAA), 34(6), 227–237. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3169
Issue
Section
Articles
