Riding Smarter: Forecasting Methods Powering Citi Bike's NYC Operations

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Published: Apr 10, 2019
DOI: https://doi.org/10.61841/turcomat.v10i1.14923
Keywords:
Bike-sharing, Demand Forecasting, Citi Bike, Machine Learning, Time Series Analysis, Urban Transportation

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Vijay Kumar Reddy Voddi
Graduate Research Assistant, Data Science Institute, Saint Peters University, 2641 John F. Kennedy Boulevard, Jersey City, NJ 07306

Abstract

The rapid growth of bike-sharing systems in urban environments necessitates efficient operations to meet user demand and optimize resource allocation. This article examines the forecasting methods employed by Citi Bike in New York City to enhance operational efficiency. By leveraging time series analysis, machine learning algorithms, and real-time data, Citi Bike can predict demand patterns, optimize bike redistribution, and improve user satisfaction.

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How to Cite
Reddy Voddi, V. K. . (2019). Riding Smarter: Forecasting Methods Powering Citi Bike’s NYC Operations. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 10(1). https://doi.org/10.61841/turcomat.v10i1.14923
Issue
Vol. 10 No. 1 (2019)
Section
Research Articles
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