Tag Archives: micromobility

Recent research

Is competition for losers in bikesharing?

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The rise and fall of the bikesharing industry in China offers a cautionary tale about the risks of an unregulated market with a low entry barrier. It is well known that, while low entry barriers can promote competition and innovation, they may also lead to higher market volatility and potential challenges in achieving profitability due to intensified rivalry . There are also limited economies of scale to be had, making it exceedingly difficult to establish a monopoly. As Peter Thiel noted, “competition is for losers”‘ in such markets and good entrepreneurs should simply stay away from them.   However, writing off the bikesharing industry as unprofitable cannot be the only story here. After all, bikesharing has a genuinely positive societal impact and should have its place in many of our cities that are haunted by the disease of auto-dependency. The question is what, if anything, can be done to foster a healthy bikesharing market that is attractive to both users and private investors.  We set up to answer this question here.  You may download a preprint here, or read the abstract below.

Abstract: We model inter-operator competition in a dockless bikesharing (DLB) market as a non-cooperative game. To play the game, a DLB operator sets a strategic target (e.g., maximizing profit or maximizing ridership) and makes tactical decisions (e.g., pricing and fleet sizing). As each operator’s payoff and decision set are influenced by its own decisions as well as those of its competitors, the outcome of the game is a generalized Nash equilibrium (GNE). To analyze how competition may shape the choice of strategic targets, we further augment the game framework with a ranking scheme to properly evaluate the preference for different targets. Using a model calibrated with empirical data, we show that, if an operator is committed to maximizing its market share with a budget constraint, all other operators must respond in kind. Otherwise, they would be driven out of the market. When all operators compete for market dominance, Moreover, even if all operators agree to focus on making money rather than ruinously seeking dominance, profitability still plunges quickly with the number of players. Taken together, the results explain why the unregulated DLB market is often oversupplied and prone to collapse under competition. We also show this market failure may be prevented by a fleet cap policy, which sets an upper limit on each operator’s fleet size.

Recent research

How many are too many?

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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.