A Survey on Trajectory-Based Carpool Ride Matching System using Map APIs

Urban mobility systems are increasingly burdened by traffic congestion, excessive fuel consumption, and environmental degradation due to inefficient vehicle usage. Conventional ride-sharing platforms rely primarily on proximity-based matching, which often results in suboptimal ride allocation and increased travel time. This paper proposes a Trajectory-Based Carpool Ride Matching System that leverages real-time map services and route alignment techniques to enhance ride- sharing efficiency. The system matches passengers with drivers based on route similarity rather than geographic proximity, thereby minimizing detours and improving travel efficiency. A multi-criteria Quality of Experience (QoE) model is incorporated to evaluate ride options based on factors such as cost, safety, travel time, and environmental impact. Additionally, a modified route optimization approach inspired by the Traveling Salesman Problem (TSP) is applied to optimize pickup and drop-off sequences. The system is implemented using a full-stack architecture with modern web technologies and integrates real-time communication mechanisms.