This is a repository created for a university project at the University of Twente.
Aims and Scope: Our project aims to analyze the factors influencing taxi tipping behavior in New York City. We explore various aspects such as area of the ride, time of day, distance traveled, and traffic conditions to understand tipping behavior patterns.
Research Questions:
- Influence of Area on Tipping: Investigate how tipping behavior varies based on the area of the ride.
- Correlation between Time, Distance, Traffic and Tipping: Explore how tipping behavior during different times of the day correlates with factors such as distance traveled, traffic conditions, and duration of the ride.
- Changes in Ride Distribution Throughout the Day: Visualize how the distribution of ride destinations changes over the course of a day.
About The Datasets:
- Yellow Taxi Trip Records: This dataset spans approximately 15 years from January 2009 to September 2023, sourced from Kaggle. It contains detailed information such as trip distances, pickup, and drop-off times.
- New York City Buildings Database: This dataset categorizes urban structures into 11 types, providing spatial context to our analysis by examining the relationship between urban environment characteristics and tipping patterns.
Methodology:
- Data Preparation: Cleaned and prepared datasets, ensuring collaborative permissions for team members.
- Data Processing: Translated coordinates of buildings, classified trip destinations based on nearby buildings, and repartitioned data for thorough analysis.
- Clean Data: Identify outliers and faulty data and filter them.
- Related Works Research: Conducted extensive research to contextualize findings within existing literature on taxi tipping behavior.
- Visualization: Created visualizations (stacked bar charts, line charts) to present findings clearly.
Thank you for your interest in our project on taxi tipping behavior in New York City. We hope our analysis proves insightful and valuable for your research or application purposes.