Smart Ride Aggregation Platform for Real-Time Comparison of Ola, Uber And Rapido Services

Abstract

The rapid growth of ride-hailing services such as Ola, Uber, and Rapido has significantly improved urban transportation by providing convenient and on-demand mobility solutions. However, these platforms often exhibit dynamic and inconsistent pricing for the same route due to factors such as traffic conditions, demand fluctuations, time of booking, and surge pricing mechanisms. This variability creates confusion for users and makes it difficult to identify the most cost-effective ride option. To address this challenge, this project proposes a Smart Ride Fare Comparison System using Machine Learning, which predicts and compares ride fares across multiple platforms in a unified interface. The system utilizes historical ride data to train machine learning models capable of estimating fares based on input parameters such as pickup location, drop location, distance, traffic conditions, and vehicle type. The proposed solution integrates multiple predictive models and applies optimization techniques to recommend the most economical ride option. A user-friendly web application is developed to allow users to enter ride details and instantly receive fare predictions along with a comparison of available services. This system enhances pricing transparency, reduces user effort, and supports data-driven decision-making. By leveraging machine learning and multi-platform integration, the project provides an efficient, scalable, and intelligent solution for selecting affordable ride-hailing services in real-time