Building an AI-Enabled Carpooling Platform to Optimize Shared Transportation Systems

The number of private automobiles, their high fuel consumption, and the pollution they contribute to the environment have all expanded significantly along with urbanization and human demands.As a remedy for these issues, carpooling has become popular.Numerous carpooling programs have also demonstrated inefficiencies, such as poor participation, restricted development, and ride matching.This study presents a carpooling software with AI capabilities intended to improve shared transportation networks.Real-time data processing and intelligent decision-making are used in the suggested system to increase ride-matching accuracy.Time limits, user preferences, historical travel patterns, and route similarities are just a few of the variables it takes into account.The platform also incorporates predictive analytics and real-time communication to maximize vehicle usage, save trip expenses, and boost operational efficiency.When compared to traditional approaches, experimental findings show that the AI-driven strategy considerably improves ride-matching performance, reduces traffic congestion, and cuts carbon emissions.The research indicates that by encouraging sustainability and improving the commuter experience overall, intelligent carpooling platforms can be extremely important to the development of future urban transportation systems.The text might be updated to better adhere to the standards of your intended publication, enhance technical rigor, or improve clarity.