Enhancing operational and infrastructure integration in shared mobility

The carpooling system is an automated system that eases travelers’ misery and helps them find cars quickly. One application that will be turbocharged is carpooling, where solo drivers to work can ask other passengers in our application for a ride. It provides the car user with an easy-to-use platform between the car owner and the car user. The existing carpooling schemes aim to reduce carbon footprint and environmental impact by matching passengers and drivers along the way. However, they are generally severely deficient in features, including inefficient static route planning, low-quality ride-matching algorithms, and a lack of high-quality user trust mechanisms. Our proposed system can resolve these problems by operating algorithmically (e.g., using A-Star, which dynamically adjusts to optimize routes based on the real environment) and by leveraging advanced machine learning models, such as clustering and recommendation systems, to improve the precision of ride matching. We also enhance the trust feature by providing comprehensive profiles of drivers with verified contact details, vehicle condition reports, user ratings, and reviews, thereby offering an efficient, dependable, and secure carpooling experience.