Stable twosided satisfied matching for ridesharing system based on preference orders Ridesharing has emerged as an alternative transportation mode along road networks around the world. Rideshare matching problem is vital to improve the sustainable development of ridesharing systems. This paper aims to address the stable twosided satisfied matching problem considering the participantsxe2x80x99 psychological perception. First of all, we investigate the elements that influence passengersxe2x80x99 and driversxe2x80x99 ridesharing experience by means of semistructured research interviews and questionnaire survey. Two ridesharing perception evaluation systems are originally established to get the preference orders of passengers and drivers separately. Then, rideshare matchingrelated definitions are stated and rideshare matching linguistic information processing is also elaborated in detail based on preference utility function, disappointment function, as well as elation function. Furthermore, we propose a stable twosided satisfied matching model on account of fuzzy linguistic information processing about ridesharing, which is able to reflect participantsxe2x80x99 psychological factors. To verify the validity of our model, we present a twosided matching case based on hypothetical rideshare matching platform. The analytical results indicate that the use of stable twosided satisfied matching method based on fuzzy linguistic information enables to substantially satisfy both driversxe2x80x99 and passengersxe2x80x99 expectation and improve the sustainability of ridesharing systems.

Reverse Auction Based Incentive Order Matching Mechanism for RealTime RideSharing Ridesharing has played an important role in reducing travel costs and global pollution. However, existing works of online matching passengersu0027 orders with vehicles usually aims to minimize the total travel distance of drivers or maximize the profit made by the platform running the ridesharing service. They ignore the fact that vehicle drivers are usually selfish and heterogeneous, and intend to maximize their own payoffs. In this paper, we intend to solve this online matching issue with the aim of maximizing the social welfare of the platform and vehicles. Specifically, we propose two incentive order matching mechanisms based reverse auction (i.e. MSWRVCG and MSWRGM), where the vehicle drivers bid for the orders published by the platform to accomplish the order while making profits. We theoretically prove the related properties of our mechanisms, such as truthfulness, individual rationality, budgetbalance, etc. We then evaluate the performance of the mechanism based on the real order data of taxis in New York City and demonstrate that our mechanisms can achieve higher social welfare than the stateoftheart method which is adopted by industry. Furthermore, we find MSWRVCG can achieve higher payoff for drivers than MSWRGM and MSWRGM can balance the effectiveness of social welfare and computational efficiency well.