An Efficient Ridesharing Model using Machine Learning Based on Riders Reviews

Abstract

It is now more important than ever to take action to lessen the negative consequences of private vehicles. If successfully implemented, mass transit is the ideal option, however because of its lack of door-to-door service, lengthier fixed routes, and unreliable timetable, many people do not appreciate it. Therefore, new facilities or services should be created to offer users a comfortable and dependable service and to lessen potentially dangerous environmental effects like pollution, congestion, etc. One of the cutting-edge technologies that is being used all over the world is ride sharing, in which users who have the same origin-destination and journey time are matched and share the transport. To help in implementation of ride sharing, a mobile application is being developed using machine learning techniques such as the Naïve Bayes algorithms to match users based on their travel preferences and habits. The app will provide a more personalized and convenient service to the users, ensuring that they are matched with the most suitable carpool partners.