A General Matching Theory of Ride-hail


Mobile internet has brought a disruptive innovation to the ride-hail industry. The technology introduced by Uber, referred to as e-hail, matches passengers with drivers through their smart phones, while integrating transaction and feedback in a single app. In comparison to taxis hailed off street, or street-hail (s-hail hereafter), e-hail has been widely praised for not only improving user experience, but also boosting productivity. This dissertation focuses on deepening our understanding on the nature and limit of both ride-hail modes by proposing a general matching theory of ride-hail. This theory, along with calibration methods and empirical evidences, not only shows that the passenger-driver matching process in ride-hail is indeed dictated by two primary physical limitations: the passengers' ability to access distant vacant vehicles and the drivers' (or the platform's) preference for certain locations, but further discovers that the revolution of e-hail is a tale of two markets: On the one hand, by expanding passengers’ access to vacant vehicles and vice versa, e-hail dramatically improves matching efficiency. In low-density markets particularly, where both demand and supply for ride-hail are low, this advantage substantially lowers the likelihood of unpleasantly long waits. On the other hand, connecting a large number of waiting passengers to the same pool of unmatched vacant vehicles induces competition among passengers, which severely limits e-hail's ability to exploit economies of scale in matching. The impact of this loss in scalability with e-hail will become more prominent in high-density markets, where arguably efficiency matters the most.

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