Paper Contents
Abstract
In today's fast-paced urban environment, bike taxis have emerged as a popular and efficient mode of transportation, offering a quick and economical alternative to traditional transport options. However, the rapid growth of bike taxi services has led to a diverse range of pricing models, making it challenging for consumers to identify the most cost-effective choices. This project, titled "Smart Ride, Smart Price: Comparing Bike Taxi Service Rates," aims to provide a comprehensive analysis of the pricing structures across various online bike taxi platforms. By collecting and comparing data on fare rates, surge pricing, and additional costs from multiple service providers, this study seeks to empower consumers with the information needed to make informed decisions. The project also explores the factors that influence pricing, such as distance, time of day, and demand, offering insights into how riders can optimize their travel costs. Ultimately, "Smart Ride, Smart Price" serves as a valuable resource for urban commuters looking to maximize value while minimizing expenses in their daily transportation needs. Using Python, we employ web scraping techniques to gather fare data from multiple online bike taxi services, ensuring a comprehensive dataset. The data is then cleaned, normalized, and analyzed using libraries such as Pandas and NumPy to identify key pricing trends and variations. Machine learning models, including linear regression, are applied to predict fare prices based on factors like distance, time of day, and demand, offering insights into pricing dynamics.
Copyright
Copyright © 2024 K.SAIKUMAR. This is an open access article distributed under the Creative Commons Attribution License.
