ECONOMETRIC ANALYSIS OF BIKE SHARING SYSTEMS

Abstract

I study the efficiency of the dockless bike sharing system, and how to utilize the operational decisions to improve system efficiency and profit.

In the first chapter, I empirically analyze riders' economic incentives in a dockless bike sharing system and explore how to improve the efficiency of this business model. Specifically, I aim to answer three main questions: (i) What is the impact of the number of bicycles in the system on efficiency? (ii) How can bike relocation be best used to improve utilization? (iii) How do the efficiency of dockless and dock-based systems compare? To address these questions, I first build a microeconomic model of user decision-making in a dockless bike sharing system. I then use this model together with transaction-level data from a major dockless bike-sharing firm to structurally estimate the customer utility and demand parameters. Using this estimation in counterfactual analysis, we find that the company can decrease the bicycle fleet size by 40% while maintaining 90% of transactions, leading to estimated savings of $6.5 Million. We further find that a spatial bicycle rebalancing system based on our customer utility model can improve daily transactions by approximately 19%. Finally, we demonstrate that without bicycle redistribution, a smartly designed dock-based system can significantly outperform a dockless system. Our model provides a utility-based model that allows companies to estimate not only transactions, but also the time and location of lost potential demand, which can be used to make targeted improvements to the geographic bike distribution. It also allows managers to fine-tune bicycle fleet sizes and spatial rebalancing parameters. Further, our structural demand modeling can be used to improve the efficiency of dock-based systems by helping with targeted dock location decisions.

In the second chapter, using data from a major dockless bike sharing system in Beijing, I study the subscription behavior and its relationship with the service level and price. Specifically, I develop an econometric model to study the subscription behavior for both existing subscribers and new sign-ups, respectively, and build up a functional relationship between the service level and system demand level and reveal the dynamics and interplays among subscription, system demand, and service level, which helps to recover the evolution of the number of subscribers over time. Based on all the estimation results and functional relationships, I then construct an empirical framework and straighten out the relationship between company profit and bicycle fleet size/subscription price. The counterfactual results show that the marginal benefit of deploying a larger bicycle fleet is decreasing, and the company should be cautious in determining and adjusting the bicycle fleet size. In addition, the examination demonstrates that the current price is too low, and raising the price properly can achieve about a 25% profit increase. The results also show the value of understanding riders' sensitivity to price and how to use it to better their operational decisions and accomplish better financial results. The counterfactual framework I proposed can be utilized in various policy evaluations and provides important insights and recommendations to the bikeshare companies and regulators.