Abstract
A variety of matching functions have been devised for ride-sourcing markets, such as perfect matching functions, Cobb–Douglas-type matching functions, queuing models, and physical models. However, less is known about the applicability and performance of these matching functions; that is, under what conditions each function effectively characterises a real market. This chapter conducts a series of simulation-based sensitivity analyses to calibrate, validate, and compare the most common matching functions, and to ascertain under what conditions they are applicable. Thus, a simulator is established to generate 420 scenarios of a ride-sourcing market with various levels of supply and demand, and a few widely used matching functions are compared in terms of their key performance metrics—matching rate, passengers' average matching time, passengers' average pick-up time, and passengers' average total waiting time—under various market scenarios.
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