A Greedy Algorithm-based Approach for Dynamic Carpooling Matching and Route Selection in Ride-hailing

Internet ride sharing allows multiple passengers to share a trip in the same vehicle, enabling cost sharing as well as reducing traffic congestion. However, existing technological limitations and uncertainties in the service (e.g., uncertainty in driver and passenger locations) make it difficult to achieve accurate and efficient real-time responses for ride-sharing matching. Balancing the optimal solutions of drivers, passengers, and platforms, dynamically matching passengers and drivers, and planning optimal paths are complex challenges. Therefore, this paper proposes a greedy algorithm based on the nearest match insertion operation to synthesize the interests of platforms, drivers and passengers. Compared with static one-time matching, this algorithm can effectively realize dynamic matching of drivers and passengers, meet real-time demand, provide drivers with optimal driving paths, and improve the scheduling efficiency of the platform. In this thesis, a dynamic carpooling optimization model is constructed and used to design comparison experiments with the traditional greedy algorithm. This study helps improve the efficiency of the ride-hailing system and enhance the passenger experience, providing valuable references for the promotion and application of dynamic carpooling models in smart cities.