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In this paper, we study a challenging problem of how to pool multipleride-share trip requests in real time under an uncertain environment. The goalsare better performance metrics of efficiency and acceptable satisfaction ofriders. To solve the problem effectively, an objective function thatcompromises the benefits and losses of dynamic ridesharing service is proposed.The Polar Coordinates based Ride-Matching strategy (PCRM) that can adapt to thesatisfaction of riders on board is also addressed. In the experiment, largescale data sets from New York City (NYC) are applied. We do a case study toidentify the best set of parameters of the dynamic ridesharing service with atraining set of 135,252 trip requests. In addition, we also use a testing setcontaining 427,799 trip requests and two state-of-the-art approaches asbaselines to estimate the effectiveness of our method. The experimental resultsshow that on average 38% of traveling distance can be saved, nearly 100% ofpassengers can be served and each rider only spends an additional 3.8 minutesin ridesharing trips compared to single rider service.

A Ride-Matching Strategy For Large Scale Dynamic Ridesharing Services Based on Polar Coordinates