Existing studies on the formulation of regulations for online ride-hailing platforms merely see the process as a struggle between interest groups. They do not address how policymakers perceive this struggle and act on their own initiative to govern these platforms. This study supplements existing studies by exploring how the metropolitan governments of two Chinese cities, Beijing and Shanghai, perceived conflicts between contending forms of chauffeur businesses and brought in regulations for new platform ventures. This thesis employs a policy change approach in the Chinese authoritarian context and reaches three conclusions. Firstly, it explains that the "special interests" of taxi entities institutionalized by the old regulatory regimes for taxi businesses incentivized the two metropolitan governments to protect taxi entities. Thus, even if Beijing and Shanghai had different first responses towards platforms with one initially emphasizing "cracking-down" and the other working on a "loose" regulatory approach, they adopted similar platform-capping policies. Secondly, this thesis finds that the two metropolitan governments cautiously disobeyed the central government's "loose" directives for platforms by combining their capping policies with selectively implementing a central directive of differentiating the markets of ride-hailing platforms and taxi operators. Thirdly, this thesis addresses obstructions to the establishment of "new regulation" that respects the business logic of platforms, which is proposed by the platform coalition. It argues that the interaction between the vested "special interests" and the fragmentation of authority makes local governments resistant to this "new regulation." / Graduate

Existing literature on the relationship between ridehailing (RH) and transit services is limited to empirical studies that rely on self-reported answers and lack spatial and temporal contexts. To fill this gap, the research takes a novel approach that uses real-time geospatial analyzes. Using this approach, we estimate the extent to which ride-hailing services have contributed to the recent decline in public transit ridership. With source data on ridehailing trips in Chicago, Illinois, we computed the real-time transit-equivalent trip for the 7,949,902 ridehailing trips in June 2019; the sheer size of this sample is incomparable to the samples studied in existing literature. An existing Multinomial Nested Logit Model was used to determine the probability of a ridehailer selecting a transit alternative to serve the specific origin-destination pair, P(Transit|CTA) . The study found that 31% of RH trips are replaceable, 61% are not replaceable, and 8% lie within the buffer zone. We measured the robustness of this probability using a parametric sensitivity analysis, and performed a two-tailed t-test, with a 95% confidence interval. In combination with a Summation of Probabilities, the results indicate that the total travel time for a transit trip has the greatest influence on the probability of using transit, whereas the airport pass price has the least influence. Further, the walk time, number of stops in the origin and destination census tracts, and household income also have significant impacts on the probability of using transit. Lastly, we performed a Time Value Analysis to explore the cost and trip duration difference between RH trips and their transit-equivalent trips on the probability of switching to transit. The findings demonstrated that approximately 90% of RH trips taken had a transit-equivalent trip that was less expensive, but slower. The main contribution of this study is its thorough approach and fine-tuned series of real-time spatial analyzes that investigate the replaceability of RH trips for public transit. The results and discussion intend to provide perspective derived from real trips and encourage public transit agencies to look into possible opportunities to collaborate with ridehailing companies. Moreover, the methodologies introduced can be used by transit agencies to internally evaluate opportunities and redundancies in services. Lastly, we hope that this effort provides proof of the research benefits associated with the recording and release of ridehailing data. / Master of Science / Existing literature on the relationship between ridehailing (RH) and transit services is limited to empirical studies that rely on self-reported answers and lack spatial and temporal contexts. To fill this gap, the research takes a novel approach that uses real-time geospatial analyzes. Using this approach, we estimated the extent to which ride-hailing services have contributed to the recent decline in public transit ridership. With source data on ridehailing trips in Chicago, Illinois, we computed the real-time transit-equivalent trip for the 7,949,902 ridehailing trips in June 2019; the sheer size of this sample is incomparable to the samples studied in existing literature. An existing Multinomial Nested Logit Model was used to determine the probability of a ridehailer selecting a transit alternative to serve the specific origin-destination pair, P(Transit|CTA) . The study found that 31% of RH trips are replaceable, 61% are not replaceable, and 8% lie within the buffer zone. We measured the robustness of this probability using a parametric sensitivity analysis, and performed a two-tailed t-test, with a 95% confidence interval. In combination with a Summation of Probabilities, the results indicate that the total travel time for a transit trip has the greatest influence on the probability of using transit, whereas the airport pass price has the least influence. Further, the walk time, number of stops in the origin and destination census tracts, and household income also have significant impacts on the probability of using transit. Lastly, we performed a Time Value Analysis to explore the cost and trip duration difference between RH trips and their transit-equivalent trips on the probability of switching to transit. The findings demonstrated that approximately 90% of RH trips taken had a transit-equivalent trip that was less expensive, but slower. The main contribution of this study is its thorough approach and fine-tuned series of real-time spatial analyzes that investigate the replaceability of RH trips for public transit. The results and discussion intend to provide perspective derived from real trips and encourage public transit agencies to look into possible opportunities to collaborate with ridehailing companies. Moreover, the methodologies introduced can be used by transit agencies to internally evaluate opportunities and redundancies in services. Lastly, we hope that this effort provides proof of the research benefits associated with the recording and release of ridehailing data.

Background: In recent years, the Swedish taxi industry has undergone a number of changes as a result of increased digitalisation in a deregulated market. Most market players have emerged as a result of the freedom of establishment, which in turn has led to higher competition. Among these new entrants, the ride-hailing business model has had an impact by taking a traditional service and performing it differently. This thesis will thus examine how this approach has affected the Swedish taxi industry in more detail. Purpose: The thesis aims to increase understanding of the aspects of the ride-hailing business model that are specifically distinguished by the company Uber. The following secondary purpose is to identify the impact this specific business model has on the Swedish taxi industry as a result of Uber's establishment on the Swedish market. Method: The thesis is an abductive case study of qualitative character. The empirical data has been collected through three distinct approaches, which are the collection of scientific material, semi-structured interviews with three respondents and a Social Media Analysis consisting of data from approximately 100 independent articles and media publications. Furthermore, these three types of empirical data have been selected via a strategic selection. Conclusion: The result of this thesis shows that the specific aspects that stand out in Uber Sweden's use of the ride-hailing business model are value creation, differentiation, innovation and social acceptance. The results also show that the ride-hailing business model has influenced the Swedish taxi industry in terms of the specific aspects' influence on the development of the taxi market and on government regulations. / Bakgrund: Under de senaste åren har den svenska taximarknaden genomgått en rad förändringar till följd av en ökad digitalisering på en avreglerad marknad. Det har uppkommit flertalet aktörer på marknaden till följd av den fria etableringsrätten, som i sin tur lett till en högre konkurrens. Bland dessa nya aktörer har affärsmodellen ride-hailing fått ett genomslag genom att ta en traditionell tjänst och utföra den annorlunda. Uppsatsen kommer därmed att närmare undersöka hur detta tillvägagångssätt har påverkat den svenska taximarknaden. Syfte: Uppsatsen syftar till att öka förståelsen för de aspekter av ride-hailing-affärsmodellen som specifikt utmärker sig hos företaget Uber. Det följande sekundära syftet avser att identifiera den påverkan som denna specifika affärsmodell haft på den svenska taxibranschen till följd av Ubers etablering på den svenska marknaden. Metod: Uppsatsen är en abduktiv fallstudie av kvalitativ karaktär. Empiri har insamlats via tre distinkta tillvägagångssätt, vilka är insamlande av vetenskapligt material, semistrukturerade intervjuer med tre respondenter samt en Social Media Analys bestående av data från cirka 100 fristående artiklar samt mediala publikationer. Vidare har dessa tre typer av empiriska data valts ut via ett strategiskt urval. Slutsats: Resultatet från denna uppsats visar att de specifika aspekterna som utmärker sig inom Uber Sveriges användning av ride-hailing-affärsmodellen är värdeskapande, differentiering, innovation och social acceptans. Vidare visar resultatet på att ride-hailing-affärsmodellen har påverkat den svenska taximarknaden i avseende på de specifika aspekternas inflytande på utvecklingen av taxibranschen samt kring statliga regleringar.

In recent years, the paradigm of personal urban mobility has radically evolved as an increasing number of Mobility-on-Demand (MoD) systems continue to revolutionize urban transportation. Hailed as the future of sustainable transportation, with significant implications on urban planning, these systems typically utilize a fleet of shared vehicles such as bikes, electric scooters, cars, etc., and provide a centralized matching platform to deliver point-to-point mobility to passengers. In this dissertation, we study MoD systems along three operational directions – (1) modeling: developing analytical models that capture the rich stochasticity of passenger demand and its impact on the fleet distribution, (2) economics: devising strategies to maximize revenue, and (3) control: developing coordination mechanisms aimed at optimizing platform throughput. First, we focus on the metropolitan bike-sharing systems where platforms typically do not have access to real-time location data to ascertain the exact spatial distribution of their fleet. We formulate the problem of accurately predicting the fleet distribution as a Markov Chain monitoring problem on a graph representation of a city. Specifically, each monitor provides information on the exact number of bikes transitioning to a specific node or traversing a specific edge at a particular time. Under budget constraints on the number of such monitors, we design efficient algorithms to determine appropriate monitoring operations and demonstrate their efficacy over synthetic and real datasets. Second, we focus on the revenue maximization strategies for individual strategic driving partners on ride-hailing platforms. Under the key assumption that large-scale platform dynamics are agnostic to the actions of an individual strategic driver, we propose a series of dynamic programming-based algorithms to devise contingency plans that maximize the expected earnings of a driver. Using robust optimization techniques, we rigorously reason about and analyze the sensitivity of such strategies to perturbations in passenger demand distributions. Finally, we address the problem of large-scale fleet management. Recent approaches for the fleet management problem have leveraged model-free deep reinforcement learning (RL) based algorithms to tackle complex decision-making problems. However, such methods suffer from a lack of explainability and often fail to generalize well. We consider an explicit need-based coordination mechanism to propose a non-deep RL-based algorithm that augments tabular Q-learning with a combinatorial optimization problem. Empirically, a case study on the New York City taxi demand enables a rigorous assessment of the value, robustness, and generalizability of the proposed approaches.

In the digital age, with the accelerating pace of e-commerce, online platforms such as Amazon, Yelp, TripAdvisor, Facebook, Netflix, Uber and others have gained in prominence. Furthermore, in the wake of the COVID-19 pandemic, even businesses which were heretofore primarily brick-and-mortar have had to shift to a strong online presence in order to adapt and survive; which, while beneficial to all stakeholders, has resulted in dire challenges for the producers/service providers, platform owners, as well as consumers. In my first essay, I investigate the challenges faced by mobility as a service (MaaS) platforms such as Uber and Lyft for managing their demand and the pool of available drivers. On one hand, driver compensation issues in MaaS platforms is a highly discussed topic. On the other hand, the MaaS platforms are expanding to encompass several external businesses in search of profitability. In this chapter, I focus primarily on driver compensation issues in MaaS platforms when the platforms engage in external businesses. I find that in the majority of instances, the driver compensation reduces when the platforms get involved in external businesses; however, there are a few cases, where it leads to an increment in driver compensation, thus benefiting them. The second essay is on the impact of online reviews from digital platforms such as Yelp and TripAdvisor on business performance. Using a data set from Yelp, first, I study the interaction of average rating and number of reviews on business performance; second, how competition affects the interaction effect of the average rating and number of reviews on the focal business' performance. I find that the impact of the interaction of average rating and number of reviews on business performance is different at various levels of average ratings, and the inclusion of competition negatively influences the interaction effect of the average rating and number of reviews on the performance of the focal restaurant. In my third essay, I analyze how the interaction of supplier encroachment and consumer showrooming impacts an omnichannel retailer and her upstream manufacturer, who encroaches the downstream retailer's market with an online direct sales channel. I identify different scenarios in a covered market where either the retailer, or the manufacturer, or both will be better off. Taken together, these three essays provide valuable managerial insights for real world business problems, which will empower researchers in academia and industry managers, and help them improve their businesses and maximize their operational performance. / Business Administration/Marketing

The advent of on-demand transport modes such as ride-hailing and microtransit has challenged forecasters to develop new methods of forecasting the use and impacts of such modes. In particular, there is some professional disagreement about the relative role of activity-based transportation behavior models -- which have detailed understanding of the person making a trip and its purpose -- and multi-agent demand simulations which may have a better understanding of the availability and service characteristics of on-demand services. A particular question surrounds how the relative strengths of these two approaches might be successfully paired in practice. Using daily plans generated by the activity-based model ActivitySim as inputs to the BEAM multi-agent simulation, we construct nine different methodological combinations by allowing the choice to use a pooled ride-hail service in ActivitySim, in BEAM with different utility functions, or in both. Within each combination, we estimate ride-hailing ridership and level of service measures. The results suggest that mode choice model structure drastically affects ride-hailing ridership and level of service. In addition, we see that multi-agent simulation overstates the demand interest relative to an activity-based model, but there may be opportunities in future research to implement feedback loops to balance the ridership and level of service forecasts between the two models.

No description available.

This dissertation consists of three essays on the empirics of firms' strategies in new industries. In the first chapter, I study the spatial mismatch between consumers and bikes in the dockless bike-sharing industry and an externality exacerbating the problem: when a consumer uses a bike for a low and inflexible price, she both displaces another consumer's usage for a potential higher-value trip, and may ride the bike to unpopular destinations. With a trip-level dataset of a bike-sharing company in Beijing, China, I develop a spatial structural model to estimate the demand for bikes with search frictions and local matchings. Compared to the scenario in which consumers always get bikes immediately, I find that local spatial mismatch between consumers and bikes reduces the total usage by 29.95%, or a net loss of 332,979 trips. Counterfactual analyses show that (1) doubling the number of bikes increases the trip volume by 28.46% while halving the number of bikes decreases the trip volume by 46.40%; (2) price-discriminating against short trips by 2% increases the total trip time by 0.22%; and (3) changing the frequency of bike reshuffling does not have a significant impact on the total usage of bikes. In the second chapter, I study how efficient capital markets are in supplying funds to new firms by looking at how a platform start-up, ofo, made its investment decisions in response to capital infusions. I fit the business performance of ofo, a bike-sharing platform start-up, in China and show how its financial conditions affected investment decisions. I analyze the effects of funding rounds from venture capitalists on the investment and business of the company with an event study framework. My estimates find that the firm increased its users and bikes by about 40% two weeks before receiving funds, suggesting that it spent much more on bike fleet and promotional offers in expectation of capital infusions. I also show that such boosts in business performance were short-lived: the number of trips and users often returned to normal levels two weeks after the funding day. My findings suggest that the capital market is inefficient in supplying funds to start-up companies. In the third chapter, I study the shakeout in the U.S. automobile industry with data retrieved from old annals of the automobile industry. I simulate a research productivity model and see if I could successfully trigger a shakeout. I find that only the cost reduction from technology advancements is not enough to trigger an industry shakeout and propose that more extreme settings are needed for further studies.

<p dir="ltr">Peer-to-peer (P2P) platforms have experienced remarkable growth, driven by advancements in internet technology and mobile applications. This rapid expansion has reshaped markets and introduced complex dynamics that warrant deeper exploration. This dissertation focuses on three critical dimensions of this field: the impact of platform introduction, platform regulation, and environmentally sustainable platform operations.</p><p dir="ltr">First, we study how the emergence of ride-hailing platforms has impacted the automotive industry by influencing both the sales and rental markets. Dealers and rental agencies, which once operated in separate markets, have become indirect competitors because car owners in the sales market offer rides to consumers in both the sales and rental markets through the platform. Therefore, to fully understand the platform’s impact, it is essential to consider these markets simultaneously. To this end, we develop a comprehensive model incorporating the manufacturer, dealer, and rental agency to analyze how a platform’s presence influences firm decisions and total car ownership. We show that the dealer increases its orders for products with high marginal costs due to the value enhancement effect, wherein car ownership becomes more valuable with the presence of a platform. Importantly, we find that neglecting the rental market - as most of the existing literature does - underestimates this effect. While the value enhancement effect does not extend to the rental market, a platform's presence may motivate the rental agency to increase its orders for products with low marginal costs and new-car valuation. However, the increase in rental cars is generally relatively modest compared to the decrease in personally owned cars, resulting in an overall increase in total ownership only for products with sufficiently high marginal costs and rental-car valuation. Moreover, we show that failing to consider both markets and their interactions may lead to inaccurately assessing the total change in ownership compared to the platform's absence. Finally, we discuss the implications of car owners' partial or heterogeneous participation rate in the platform and demonstrate that our results generally hold.</p><p dir="ltr">Second, we focus on the 90-day cap regulation in San Francisco and Berkeley to investigate the effectiveness of this supply restriction in improving the affordability of housing in the city. We specifically investigate 1) whether the regulation accurately targets landlords in the sharing market and increases the supply in the local long-term rental market and 2) whether the regulation achieves its goal of making housing more affordable for the targeted lower-income population in the city. We exploit a detailed dataset on Airbnb and Zillow in this empirical analysis. Using standard DID regression analysis, the paper finds that the regulation significantly decreased the listing number by about 29.6% and increased the overall average daily rate of short-term rentals by about 14.6% on the platform while decreasing the average price of long-term rentals by about 4.1% in the local residential market, in the year following the enforcement of the regulation. Meanwhile, we find that the benefit of the regulation effectively targeted affordable homes in the long-term rental market but did not affect the high-end and single-family markets significantly. In particular, using quantile DID methods, we show that the regulation only reduces the average rental price (of all types of homes) in only about 30% of the lower end of the local long-term rental market. The regulation also made a heterogeneous impact on different types of listings on the platform, making hosted listings increase their supply and benefit from the spillover effects, especially since it works efficiently to figure out landlords and sharers for multi-home host listings. </p><p dir="ltr">Third, we examine a ride-hailing platform's optimal subsidy design to increase electric vehicle (EV) adoption among drivers, which has been a key operational goal for P2P platforms as they increasingly prioritize sustainability. To this end, we model the choices made by drivers when selecting between gasoline gasoline vehicles (GVs) and EVs, considering the heterogeneity of drivers in their time costs. We examine how market segments are shaped by differences in the marginal costs of usage and prices between the two types of vehicles. These analyses reveal the distinct trade-offs faced by drivers with high supply compared to those with low supply. Motivated by practice, we consider three types of subsidies a platform may adopt to achieve full adoption of EVs, i.e., set-up bonuses, earning boosts, and charging discounts. We find that the earning boost subsidy consistently drives greater EV usage than the other subsidy types. However, we also find that a profit-driven platform is more likely to favor earning boosts when its per-unit profit is relatively high, even if this may not align with the most environmentally beneficial outcomes. This highlights the need for careful consideration of the platform’s subsidy design, as profit-maximizing strategies might conflict with environmental objectives. </p>

<p dir="ltr">Ride-hailing is the frontier of transportation innovations and has become an essential component of urban mobility. Addressing the efficiency of service operations and associated challenges has significant implications for the future of transportation systems. </p><p dir="ltr">In this dissertation, we develop a series of approaches and tools to enhance the efficiency of ride-hailing systems, validate operational controls, and assess the associated risks. Through extensive numerical experiments, we demonstrate the efficacy of our methods.</p><p dir="ltr">In Chapter 2 to 4, we develop three complementary operational algorithms aimed at improving ride-hailing services' efficiency:</p><ul><li> Chapter 2 focuses on proactive vehicle repositioning using a supervised learning model to recommend optimal pickup locations for vacant vehicles. The numerical experiments suggest this strategy can reduce empty vehicle mileage and also balance driver income/utilization.</li><li>Chapter 3 presents a hub-based ride-sharing algorithm that features an efficient data structure for querying feasible vehicle schedules and employs model predictive control to account for future demand and supply uncertainties. This approach significantly outperforms baselines that do not account for future supply and demand or rely on point-wise predictions.</li><li>Chapter 4 addresses dynamic pricing in ride-hailing systems. We contribute to a rigorous definition of the problem and a reinforcement learning-based method to generate deterministic pricing policies. The numerical results suggest our approach can achieve near-optimal performance in promoting service income by effectively reducing empty vehicle mileage.</li></ul><p dir="ltr">Chapter 5 introduces METS-R SIM, an agent-based simulator that combines detailed microscopic traffic simulation models with dynamic demand-supply matching. We validate METS-R SIM against actual ride-hailing data, demonstrating its ability to accurately reproduce travel time and distance profiles and provide valuable insights for improving supply design and control strategies.</p><p dir="ltr">Finally, Chapter 6 explores the security challenges in autonomous mobility-on-demand (AMoD) systems. We develop a threat model to assess risks in the passenger-vehicle matching mechanism. Our experiments reveal that optimization-based attacks can significantly degrade service quality and increase traffic congestion, highlighting the need for extensive security analyses in autonomous ride-hailing operations.</p><p>Together, these contribute to a complete framework for improving ride-hailing systems with advanced operational algorithms, high-fidelity validation, and comprehensive risk assessments, paving the path toward a more efficient and secure ride-hailing service.</p>