Koki TAKAHASHI

A Study on Matching Equilibrium Models for Shared Ride Sharing Services

Teppei KATO

In recent years, local governments and the public and private sectors have been working together to revise plans and promote the conversion to demand bus services. Under such circumstances, ridesharing services (hereafter referred to as "ridesharing") have been attracting attention in major overseas countries, and in some regions, ridesharing is becoming established as an option for transportation. Based on such ridesharing, it is necessary to reconsider how sustainable public transportation should be in the future.
In this study, we proposed a model that can derive the spatial distribution of the number of users, focusing on the combined area of existing public transportation and ridesharing services. Specifically, we formulated an equilibrium model (matching equilibrium model) defined as a fixed point problem by considering changes in costs due to carpooling and vehicle congestion, which depend on the selection probability in the transportation mode choice model.
Numerical experiments using the proposed model were conducted to analyze changes and trends in the probability of choosing ridesharing depending on the distance between the origin and destination points and the total traffic demand distribution. The results showed that when carpooling is taken into account, the change in the probability of choosing ridesharing tends to "decrease in the short-distance region and increase after a certain threshold" and "monotonically increase with distance. Furthermore, when the increase in travel time due to vehicle congestion is taken into account, in addition to the above-mentioned trends, we obtained a trend of "monotonically increasing with distance, then leveling off or decreasing at the first threshold value, and then increasing again at the second threshold value. Through numerical experiments, it became clear that the equilibrium solution of the matching equilibrium model proposed in this study could be approximated by an analytical function. We also estimated the price range of ridesharing that maximizes the social surplus calculated from the producer surplus (fee income of rideshare drivers) and consumer surplus (expected maximum utility of users), and examined changes and trends in the probability of choosing ridesharing in such cases. As a result, the price setting for ridesharing when social surplus is maximized is in the low price range, and the above three trends in the probability of selection were obtained.