Year: 2025
Type: article
Abstract: : This paper presents a comprehensive analysis of Uber trip data in New York City using data science techniques to improve ride-hailing efficiency. The project utilizes historical trip data from the NYC High Volume For-Hire Vehicle (HVFHV) dataset, incorporating clustering (K-Means) and prediction (Random Forest Regression) to forecast demand patterns. It also integrates Power BI for real-time visualization and insights. The aim is to optimize driver allocation, reduce passenger wait times, and enhance urban mobility.
Author: Shanthini D
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410857678