Ride-sharing application systems can reduce traffic congestion and alleviate parking space tension by increasing the utilization rate of car available seat capacity， thus improving social and environmental benefits. The effective real-time matching and optimization technology of drivers and passengers is one of the core components for a successful ride-sharing system. Role-Based Collaboration （RBC） is an emerging methodology to facilitate an organizational structure， provide orderly system behavior， and coordinate the activities within the system. In order to reduce the dynamic real-time matching time of passengers and drivers， and improve the matching efficiency， a method combining RBC and Environment-Class， Agent， Role， Group and Object （E-CARGO） model was proposed to formalize ride sharing problem. To improve the utilization rate of available seat capacity， maximize platform revenue， and rationalize resource allocation with constraints of entire resource capacity and given profit， the modeling and simulation experiments for ride-sharing matching method were conducted. The experimental results show that the proposed formal method based on E-CARGO model can be applied to the modeling of ride-sharing matching problem， and the optimal matching matrix and time can be obtained by Kuhn-Munkres （K-M） algorithm and ILOG software package in Java. The simulation results show that the average time of K-M algorithm is reduced by 21% at least compared to ILOG software package algorithm. When the agent size is larger than a certain value （more than 600）， the time consumption of the proposed algorithm increases sharply.