International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Predictive Modeling for Bike Rental Demand: Enhancing Operational Efficiency and User Satisfaction in Urban Mobility

Author(s) Swapnil Chaurey, Shreyash Jodhe, Priyanshu Yawalkar, Sufiyan Sheikh, Rohan Mahajan, Mrs.Kamal.S.Chandwani
Country India
Abstract This project focuses on developing a predictive model to forecast bike
rental demand by analyzing historical rental data, weather information,
and temporal variables. The model will employ machine learning
techniques, such as regression analysis, time series forecasting, and
ensemble methods, to capture the complex relationships between these
factors and rental demand.
Keywords The growing popularity of bike rental services has made them an integral part of urban transportation systems, offering an eco-friendly and convenient alternative for short-distance travel. However, the effectiveness of these services relies heavily on the accurate prediction of bike rental demand, which can fluctuate due to various factors such as weather conditions, time of day, day of the week, and seasonal trends. Predicting this demand accurately is crucial for optimizing the availability of bikes, reducing operational costs, and improving user satisfaction.
Field Engineering
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-10
Cite This Predictive Modeling for Bike Rental Demand: Enhancing Operational Efficiency and User Satisfaction in Urban Mobility - Swapnil Chaurey, Shreyash Jodhe, Priyanshu Yawalkar, Sufiyan Sheikh, Rohan Mahajan, Mrs.Kamal.S.Chandwani - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.30486
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.30486
Short DOI https://doi.org/g8qtgx
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