Essays on Two-sided Platforms: Market Entry Strategy and Dynamic Pricing

This dissertation consists of two chapters: In the first chapter, we build a theoretical framework to study the dynamic entry interactions between two platforms with homogeneous products into city-based markets. This research is applicable for studying the entry strategies between, for example, Uber and Lyft; Groupon and Living Social, and other business models with the attributes of switching cost, network effect, and segregated markets. We address three questions in this paper: 1) What determines the expansion path of city-based platforms?; 2) What factors are affecting the market concentration structures; and 3) Under what conditions can a second mover become the market leader (with more than 50% of the market share)? We find that a significant degree of the network effect and large switching cost will build a natural barrier for the late entrant; Transaction-efficient markets with larger transaction volume are less likely to be concentrated than transaction-inefficient markets. We take consideration of entry cost and initial fund in our dynamic settings, and find that the uncertainty in market return will make the platforms\u27 expansion path and the final outcome less predictable. However, on average, the capability of capturing the largest market first is crucial for both players; if a platform loses the opportunity of being the first to capture the largest market, it may have to raise a considerable amount of money to overcome its disadvantages in the following competitions. In the second chapter, we empirically investigate the effect of the dynamic pricing system on ride-sharing platform drivers\u27 labor supply. Rather than working-hour and wage-rate relation explored by previous and current literature, we examine the instantaneous response of drivers to price surges. Using data from New York City, we estimate the structural model through a constrained non-parametric instrumental variable (NPIV) approach. We find that the emergence of a price surge is a strong incentive for drivers, and the dynamic pricing scheme of ride-sharing platforms effectively solves the geographical disparity problem of uncoordinated taxi systems. Consequently, the overall accessibility and quantity of pickup service in the entire city will increase. In the absence of dynamic pricing, we show in a counterfactual analysis that platform drivers will be clumped in the Manhattan area and airports, a dilemma shared by the taxi drivers. The counterfactual context implies that 27 % of the total supply will be lost, including a significantly large 59% reduction in the non-Manhattan area

Spatial pricing empirical evaluation of ride-sourcing trips using the graph-fussed lasso for total variation denoising

This study explores the spatial pricing discrimination of ride-sourcing trips using empirical data. We use information from more than 1.1 million rides in Austin, Texas, provided by a non-profit transportation network company from a period where the main companies were out of the city. We base the analysis on operational variables such as the waiting or idle time between trips, reaching time, and trip distance. Also, we estimate three different productivity measures to evaluate the impact of the trip destination on the driver continuation payoff. We propose the application of a total variation denoising method that enhances the spatial data interpretation. The selected methodology, known as the graph-fussed lasso (GFL), uses an l₁-norm penalty term that presents a variety of benefits to the denoising process. Specifically, this approach provides local adaptivity; it can adapt to inhomogeneity in the level of smoothness across the graph. Solving the GFL smoothing problem involves convex-optimization methods, we make use of a fast and flexible algorithm that presents scalability and high computational efficiency. The principal contributions of this research effort include a temporal and spatial evaluation of different ride-sourcing productivity measures in the Austin area, an analysis of ride-sourcing trip pricing and its effect on driver equity, and a description of the principal ride-sourcing travel patterns in the city of Austin. The main results suggest that drivers with rides ending in the central area present favorable spatial differences in productivity when including the revenue of two consecutive trips. However, the time effect was more contrasting. Weekend rides tend to provide better driver productivity measures.Statistic

A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021

On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures

The proliferation of smartphones in recent years has catalyzed the rapid growth of ride-sourcing services such as Uber, Lyft, and Didi Chuxing. Such on-demand e-hailing services significantly reduce the meeting frictions between drivers and riders and provide the platform with unprecedented flexibility and challenges in system management. A big issue that arises with service expansion is the empty miles produced by ride-sourcing vehicles. To overcome the physical and temporal frictions that separate drivers from customers and effectively reposition themselves towards desired destinations, ride-sourcing vehicles generate a significant number of vacant trips. These empty miles traveled result in inefficient use of the available fleet and increase traffic demand, posing substantial impacts on system operations. To tackle the issues, my dissertation is dedicated to deepening our understanding of the formation and the externalities of empty miles, and then proposing countermeasures to bolster system performance. There are two essential and interdependent contributors to empty miles generated by ride-sourcing vehicles: cruising in search of customers and deadheading to pick them up, which are markedly dictated by forces from riders, drivers, the platform, and policies imposed by regulators. In this dissertation, we structure our study of this complex process along three primary axes, respectively centered on the strategies of a platform, the behaviors of drivers, and the concerns of government agencies. In each axis, theoretical models are established to help understand the underlying physics and identify the trade-offs and potential issues that drive behind the empty miles. Massive data from Didi Chuxing, a dominant ride-sourcing company in China, are leveraged to evidence the presence of matters discussed in reality. Countermeasures are then investigated to strengthen management upon the empty miles, balance the interests of different stakeholders, and improve the system performance. Although this dissertation scopes out ride-sourcing services, the models, analyses, and solutions can be readily adapted to address related issues in other types of shared-use mobility services.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163209/1/xzt_1.pd