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Essays on Revenue Management in Urban Mobility


The essays in this dissertation lie at the intersection of revenue management, urban mobility, and technology. Some of the most well-studied problems in operations management and operations research have been inspired by the transportation sector. For instance, the traveling salesman problem, the vehicle routing problem, freight logistics, airline fleet planning, port operations, and rail scheduling are set in the transportation industry. In this dissertation, we restrict our analysis to urban mobility, which focuses on transportation in metropolitan cities. Urban mobility has evolved dramatically over the past decade due to advances in technology, in particular, the mobile phone. Bike-sharing, ride-sharing, and vehicle sharing are possible today because of the growth and popularity of mobile phones. Because of this growth, users are able to access train and bus schedules in real-time, pay fares, and instantaneously reserve and check-out shared cars, bikes, electric scooters, and other types of shared vehicles. While this accessibility provides users with more flexibility, the systems are also increasingly difficult to operate and manage. One way to address this operational complexity involves using tools and methods from revenue management. More generally, using price and discounts as levers to shape customer behavior in a way that improves the system's service level, revenue, customer satisfaction, and other key performance metrics.

This dissertation is made up of four essays across three chapters that address questions in operating systems in urban mobility, and we use techniques from revenue management to study how these systems can operate more effectively.

In Chapter 1, we study free-ride policies as a mechanism to incentivize users of a "dockless" or "free-floating" electric vehicle sharing system (EVSS) to park vehicles at charging stations in order to maintain a charged fleet. A balanced system has a fleet that is adequately charged and evenly dispersed throughout the city. If left to unfold naturally, the system would fall out of balance, and revenue and customer experience might suffer. Most sharing systems use manual repositioning to achieve this balance, but we consider pricing incentives as an alternative method. We develop an infinite horizon dynamic program to analyze free-ride policies. We focus on an EVSS that offers free rides to customers if they return vehicles to charging stations. We build on this initial formulation to construct a mixed-integer program that outputs intuitive, battery-threshold rules for when to offer free rides. We also extend the model to accommodate more general discount-based policies. In a discrete-event simulation model using real data from an EVSS, we compare the performance of this simple policy against other sophisticated policies, including the commonly used fine-based policy, which fines users for street-parking vehicles with low battery. We first find that the simple threshold-based policy performs close to a more sophisticated, black-box policy in terms of revenue. We also discover that the free-ride policies generate customer utilities that are ten times higher than fine-based policies, but also generate less revenue. However, free-ride policies can be less costly to implement since they rely on manual repositioning up to 65-75% less than the benchmarking policies. Our simulation reveals this three-dimensional trade-off between customer satisfaction, revenue, and operational complexity. Our results are robust under many demand patterns and under a variety of network settings.

In the remaining chapters, we are motivated by the claim that 30% of metropolitan traffic is a result of individuals searching or "cruising" for parking (Shoup, 2017). It is theorized that this cruising behavior causes superfluous traffic congestion that can be assuaged and mitigated with more effective pricing polices. In particular, pricing policies that ensure there is at least one open spot available on each block at all times under regular demand. With this in mind, Chapters 2 and 3 examine how to develop dynamic pricing policies that both maximize revenue and address traffic congestion, with Chapter 2 focusing on estimating key parameters that feed into the pricing models and Chapter 3 focusing on developing the price optimization models.

In order to develop such pricing policies, one needs to know the price and spatial elasticity of parking, where price elasticity is a measure of the change in demand in response to a price change and the spatial elasticity is a measure of how much money a customer would require to park a mile or a block away from their destination. Using data from our industry partner, a venture-backed technology company that develops a software-as-a-service (SaaS) platform to manage parking, permitting, and micro-mobility for municipalities and organizations throughout the world, we are able to empirically estimate both of these values in Chapter 2. In this chapter, the context is parking-specific and the estimates are unique to the data from our partner city. However, we believe that our approach and the estimates can be used across urban mobility applications, and beyond, as these elasticities are often assumed to be known or given in many classic revenue management problems.

In Chapter 2.1, the first essay of Chapter 2, we estimate the price elasticity after a 20% price increase in a mid-sized U.S. city and find the average price elasticity of parking demand is between -3.42 and -1.57, which is higher than existing estimates (Lehner and Peer, 2019). One reason our study could be producing higher estimates is because, as far as we know, our work is the first to use transactions data from a mobile phone application for parking payments, which is more accurate and detailed than the data used in the existing literature. With our model, we can also measure how long it takes for customers to learn about and respond to the price change. Despite the price change being publicly advertised, we find that customers do not respond to the price change until they experience it firsthand.

In Chapter 2.2, the second essay of Chapter 2, we estimate the spatial elasticity. We perform our estimation using a panel dataset of parking transactions spanning 21 months in a large U.S. city. During this time frame, there was an unannounced pricing error where two neighboring blocks were discounted by 67% for 16 months. We find that customers require approximately $81 to walk an additional mile to their intended destination. This estimate increases 13% in the presence of rain and 36% during the morning rush hour.

In Chapter 3, the final chapter of this dissertation, we study optimal, dynamic pricing policies for a system, or network, of reusable resources, where a parking spot on a city block can be interpreted as a resource that can be "reused" after it is vacated.

We focus our analysis on a single reusable product (i.e., a single zone or block with a fixed number of parking spaces) and aim to set the price as a function of the number of occupied spaces. Our objective is to maximize the long-run average revenue under Markovian assumptions (i.e., Poisson arrival and exponential usage times). In queuing theory, such a model is known as an Erlang loss system. We reformulate this objective function using a metric that we term the "conditional entry-state distribution." There does not exist a method for computing this metric, so in Chapter 3, we develop an algorithm that converges to the metric's true value for any Erlang loss system. We also provide analysis on the performance and speed of the algorithm.

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