If you had visited  big Chinese cities between 2016 and 2019, you might have noticed many sidewalks  rendered completely unwalkable by a myriad of shared bikes.  Thrown out there by well-meaning entrepreneurs and ambitious investors who funded their operations, the sight of these bikes clogging public space epitomizes the feverish quest for growth that has become the hallmark of the Chinese Model.  My sense was that most shared bike operators had no idea — or did not care —  how many bikes they need to serve a given city well and at what price they should charge the users in order to cover the operating cost, including the cost of routinely moving the bikes around to meet the demand.    However, as a transportation modeler and analyst, I  found these questions fascinating, and this paper was precisely the result of my attempt to answer them.

You can read the abstract below and a preprint can be downloaded here.

How Many Are Too Many? Analyzing Dockless Bikesharing Systems with a Parsimonious Model

Using a parsimonious model, this paper analyzes a dockless bikesharing (DLB) service that competes with walking and a generic motorized mode. The DLB operator chooses a fleet size and a fare schedule that dictate the level of service (LOS), as measured by the access time, or the walking time taken to reach the nearest bike location. The market equilibrium is formulated as a solution to a nonlinear equation system, over which three counterfactual design problems are defined to maximize (i) profit; (ii) ridership; or (iii) social welfare. The model is calibrated with empirical data collected in Chengdu, China and all three counterfactual designs are tested against the status quo. We show the LOS of a DLB system is subject to rapidly diminishing returns to the investment on the fleet. Thus, under the monopoly setting considered herein, the current fleet cap set by Chengdu can be cut by up to three quarters, even when the DLB operator aims to maximize social welfare. This indicates the city’s fleet cap decision might have been misguided by the prevailing conditions of a competitive yet highly inefficient market. For a regulator seeking to influence the DLB operator for social good, the choice of policy instruments depends on the operator’s objective. When the operator focuses on profit, limiting fare is much more effective than limiting fleet size. If, instead, they aim to grow their market share, then setting a limit on fleet size becomes a dominant strategy. We also show, both analytically and numerically, the ability to achieve a stable LOS with a low rebalancing frequency is critical to profitability. A lower rebalancing frequency always rewards users with cheaper fares and better LOS, even for a profit-maximizing operator.