Campus Carpooling: Optimized Ridesharing for Students using Hybrid Ridesharing Algorithm HRA

A student-focused carpooling service created to accommodate the unique transportation needs of college students. Other users can select where they wish to be picked up after individuals post their starting locations, available seats, and routes on the website. The service optimizes the matching process between riders and passengers using a machine learning algorithm to guarantee efficient and comfortable transportation arrangements. Dynamic pricing, which varies based on supply and demand, is one of the system’s unique features. It allows users to choose whether to pay for rides or not, based on their preferences. The platform incorporates user registration and verification to maintain security and community trust, and both riders’ and passengers’ profiles include ratings and reviews to create a reliable user base. The machine learning component improves the overall effectiveness of the service by reducing the overall journey time and distance through the use of clustering and optimization techniques. Additional features including ride monitoring, emergency contact information, identification verification, and an integrated payment channel for paid rides are offered to ensure safety. In-app messaging and notifications make it easier for users to communicate. To ensure smooth operations and quick problem-solving, an admin interface is also offered for managing users, rides, and payments. The initiative aims to establish a carpooling system that is economical, efficient, and sustainable for college students in order to encourage environmentally friendly transportation options on campuses.