A Parallel Simulated Annealing Enhancement of the OptimalMatching Heuristic for Ridesharing In this paper, we develop an efficient parallelheuristic method for solving the global optimization problemassociated with the ridesharing system. Based on the carefullyformalized problem and objective function, we fully utilize theheuristic characteristics of the algorithm for handling the reallifeconstraints in ridesharing. Following the principles of simulatedannealing, our method is adaptive in handling the matchingand route optimization tasks. We develop an efficient parallelscheme with simulated annealing, named PCSA, for solving theglobal optimization problem for ridesharing. Our algorithm iscapable to efficiently address the potential of ridesharing byexploiting the mobility information of the ride requests. Basedon extensive experiments on large realworld data, we validatethe performance of our parallel heuristic algorithm. Our resultsconfirm the effectiveness and efficiency of the proposed methodand its superiority over all other benchmarks.

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.