Exploratory Data Analytics on Uber Transportation Patterns

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.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-05-29
DOI
https://doi.org/10.22214/ijraset.2025.71576
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Exploratory Data Analytics on Uber Transportation Patterns

D Shanthini
International Journal for Research in Applied Science and Engineering Technology
Traffic Prediction and Management Techniques
article

Exploratory Data Analytics on Uber Transportation Patterns

D Shanthini
article en

Abstract

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.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(5)
Openalex Percentile: Top 88%
Traffic Prediction and Management Techniques
DOIOpen Access PDFOpenAlex
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Exploratory Data Analytics on Uber Transportation Patterns — D Shanthini · International Journal for Research in Applied Science and Engineering Technology (2025) | TGRS Research Map | TGRS