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A dynamic ridesharing system (DRS) is a system where users can find ridesharing partner(s) at any time, even shortly before making a trip. A DRS that does not consider individual preferences may cause dissatisfied matchings of users in a shared vehicle and lead to abandonment of DRS in the long term. To investigate the evolution of DRS, such as long-term adoption, this study develops a model of DRS considering the rational behavior and learning process of its users. User behavior is considered as travel mode choice and ridesharing partner choice decisions under the expected utility maximization concept. The day-to-day evolution of a DRS is simulated based on the proposed model, and the effects of user learning behaviors and some social factors pertinent to long-term DRS adoption are investigated.
Thaithatkul et al. (Tue,) studied this question.