Ride-sharing Platform Research Articles

Surge Pricing on a Service Platform Under Spatial Spillovers: Evidence From Uber

Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with demand. We develop an optimization model to characterize the relationship between surge price and spillover. We test predicted relationships using a spatial panel model on a dataset from Uber’s operation. Results reveal that Uber’s pricing accounts for both capacity and price spillover. There is a debate in the management community on the efficacy of labor welfare mechanisms associated with shared capacity. We conduct counterfactual analysis to provide guidance in regards to the debate, for managing congestion, while accounting for consumer and labor welfare through this online platform.

Entrepreneurship, innovation, and political competition: How the public sector helps the sharing economy create value

Research Summary:With the recent growth of the sharing economy, regulators must frequently strike the right balance between private and public interests to maximize value creation. In this article, we argue that political competition is a critical ingredient that explains whether cities accommodate or ban ridesharing platforms and that this relationship is moderated in more populous cities and in cities with higher unemployment rates. We test our arguments using archival data covering ridesharing bans in various U.S. cities during the 2011–2015 period. We supplement these data with semistructured interviews. We find broad support for our arguments while mitigating potential endogeneity concerns. Our study has important implications for nonmarket strategy, entrepreneurship and innovation, and public‐private partnership literatures. In addition, our findings inform policy debates on the sharing economy.Managerial Summary:Entrepreneurs and businesses oftentimes face severe regulatory barriers when commercializing innovative products and services even if the innovations are generally beneficial for consumers and the broader society. This research focuses on the political determinants of regulation to provide a better understanding of why some markets are more receptive to innovative products while other markets are more hostile to them. Using the banning of ridesharing companies (e.g., Uber and Lyft) in various U.S. cities during the 2011–2015 period, we find that elected politicians facing less political competition (i.e., not easily replaceable, serving multiple terms, longer tenure in office) were more likely to ban ridesharing companies and favor, potentially displaceable, local taxicab companies. Our research has implications for navigating the political barriers to entry.

Assessing the Impact of App-Based Ride Share Systems in an Urban Context: Findings from Austin

App-driven ridesharing platforms are gaining popularity and are transforming urban movement patterns in cities throughout the world. Because of privacy and business considerations, their owners have released little information about riders’ trip-making characteristics. This lack of data prevents planners and modelers from understanding and quantifying the impact of these new modes on regional travel patterns. In 2016, RideAustin, a not-for-profit company, was established to provide mobility-on-demand services in the Austin region. RideAustin released its dataset of over one million trips to researchers to support transportation planning through a better understanding of urban travel flows. This paper presents findings from an in-depth analysis of this dataset and summarizes key aspects of interest to the transportation research community such as the number of riders, drivers, and trips; total vehicle miles including deadhead miles; and terminal times. The paper also presents findings from two case studies that show the competitiveness of RideAustin versus transit and the utilization of the RideAustin system during the South by Southwest festival. While some of the metrics cannot be readily transferred to other regions, several findings can be used by planners and modelers as they integrate rideshare systems within their planning and modeling frameworks. We also believe that some of the research findings may provide insights into a future system of autonomous and shared vehicles.

Understanding the ridesharing needs of older adults

Riding in the comfort of a private vehicle is the preferred mode of alternate transportation, but ridesharing services might also fit the needs of older drivers. To understand the ridesharing needs of older adults, the vehicles of 39 drivers 65 years and older were instrumented for two weeks. Post-drive interviews were conducted to understand their ridesharing challenges. A Contextual Design approach was used to assess the data from the instrumentation recordings and interview responses. Results from the Contextual Design showed that older adults faced four main challenges: limited social network, efficient communication of trip details and needs, and establishing trip reliability and privacy. These findings were used to design and build a web-based ridesharing tool prototype for older adults that addressed these challenges. Implementing such tools on current ridesharing platforms and ICTs could provide more accessible mobility options, and prolong the overall mobility and independence of older adults.

Deregulation of long distance coach services in France

Long distance passenger mobility is experiencing in France a huge innovation boost. Based on Internet technology and customer-oriented services some new entrants are diversifying the options for the travelers. For instance, High Speed Rail (HSR or “TGV” in French) services are challenged by low cost airlines but also by BlaBlaCar, the world leader in ride-sharing, and since 2015 by the deregulated long distance coach services. At the end of the first quarter of 2017, 8 million passengers have already experienced long distance coaches in France. But is it enough? The aim of our paper is to analyze the development of coach services in France as a case study and their lack of profitability. Until now, coach operators are suffering huge losses. Is it transitory or can we consider that the high degree of competition between HSR, ride-sharing and coach services will structurally limit the development of the latter? How will the structure of this market evolve if the demand remains at a rather low level, in comparison with United Kingdom for instance? To conclude, it is a dynamic market as far as passengers are concerned but it can be a lose-lose game for operators such as coach operators or ride-sharing platforms.

Driver-Rider Cost-Sharing Strategies and Equilibria in a Ridesharing Program

The rapid development of smartphone technology has led to the increased popularity of dynamic ridesharing apps used to organize ad hoc ridesharing trips between strangers on short notice. To support such real-time on-demand services, cost-sharing between drivers and riders is commonly centrally determined by ridesharing apps according to prescribed rules. To highlight the impacts of appropriate cost-sharing strategies on the success of ridesharing programs, this paper models the mode choices of a group of heterogeneous travelers with continuously distributed values of time in a single-corridor network, considering the complex interactions between travelers’ mode choices and the attractiveness of ridesharing in terms of rider/driver waiting/detouring times and matching probabilities. The equilibrium state under any given cost-sharing strategy is described by a system of variational inequalities based on which the existence of equilibria is established. With the proposed modeling framework, various cost-sharing strategies are examined to avoid mode shifts among transit users to autos and/or reduce vehicular traffic in the short run; the necessary conditions for cost-sharing strategies to sustain participation and/or reduce vehicle usage are explicitly provided. It is shown that when driving alone is faster but more expensive than public transit, no cost-sharing strategy exists to sustain an active ridesharing platform without inducing transit users to join the ridesharing program. Moreover, the existence of cost-sharing strategies capable of reducing vehicular traffic on the road is not always guaranteed, depending on the costs of driving alone and taking public transit in the considered corridor, fuel prices, and travelers’ prioritization of safety and privacy. Furthermore, it is found that the initial state with no ridesharing participants is an equilibrium under any cost-sharing strategy if the additional cost incurred by a traveler through participating in a ridesharing program is nonnegative. This explains the difficulty of initiating a ridesharing program and implies the initial necessity of subsidizing all intended riders and/or drivers to encourage participation. The online appendix is available at https://doi.org/10.1287/trsc.2017.0801 .

When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms

Providing transparency into operational processes can change consumer and worker behavior. However, it is unclear whether operational transparency is beneficial with potentially biased service providers. We explore this in the context of ridesharing platforms where early evidence documents bias similar to what has been observed in traditional transportation systems. Platforms responded by reducing operational transparency through removing information about riders’ gender and race from the ride request presented to drivers. However, following this change, bias may still manifest through driver cancelation after a request is accepted, at which point the rider’s picture is displayed. Our primary research question is to what extent a rider’s gender, race, and perception of support for lesbian, gay, bisexual, and transgender (LGBT) rights impact cancelation rates. We investigate this through a large field experiment on a major ridesharing platform in Washington, DC. By manipulating rider names and profile pictures, we observe drivers’ behavior patterns in accepting and canceling rides. Our results confirm that bias at the ride request stage has been eliminated. However, after acceptance, racial and LGBT biases are persistent, while we find no evidence of gender biases. We also explore whether peak times moderate (through increased pay to drivers) or exacerbate (by signaling that there are many riders, allowing drivers to be more selective) these biases. We find a moderating effect of peak timing, with lower cancelation rates for non-Caucasian riders. We do not find a similar moderating effect for riders that signal support for the LGBT community. This paper was accepted by Vishal Gaur, operations management.

A Theory of Multihoming in Rideshare Competition

We examine competition amongst ridesharing platforms where firms compete by choosing both the price of rides and the extent of idleness. Idleness means drivers who are compensated without picking up passengers, instead acting to reduce passenger wait time. We show that when consumers are the only agents who multihome, idleness falls compared with when they face a monopoly ridesharing platform. When drivers and consumers multihome, idleness further falls to zero as it involves costs for each platform that are appropriated, in part, by their rival. Interestingly, socially superior outcomes may involve monopoly or competition under various multihoming regimes, depending on the density of the city, and the relative costs of idleness versus consumer disutility of waiting.

Artists in The Creative Economy

Today the algorithmic moves off the white- board and into the world, producing subjectivities, articulating relationships, and shaping behaviours. Yet to obtain its objectives, the algorithmic must draw upon bodies, flows, and materials — matter which is contentious and agents which have their own intentionalities. Efficacy cannot simply be assumed, but must be incessantly negotiated via a set of procedures. What are the operations needed to incorporate subjects and spaces into regimes of algorithmic coordination? By examining the ride-sharing platform Uber as a case-study, three operations are identified as critical: encapsulation, enlistment and enchantment. When these operations are incomplete, algorithmic traction on a subject slips away, producing an array of undesired and unanticipated effects.

Rendered Inoperable


 
 
 Today the algorithmic moves off the white- board and into the world, producing subjectivities, articulating relationships, and shaping behaviours. Yet to obtain its objectives, the algorithmic must draw upon bodies, flows, and materials — matter which is contentious and agents which have their own intentionalities. Efficacy cannot simply be assumed, but must be incessantly negotiated via a set of procedures. What are the operations needed to incorporate subjects and spaces into regimes of algorithmic coordination? By examining the ride-sharing platform Uber as a case-study, three operations are identified as critical: encapsulation, enlistment and enchantment. When these operations are incomplete, algorithmic traction on a subject slips away, producing an array of undesired and unanticipated effects.
 
 

Digital Protectionism in South Korea -Cases from Online Game Industry, Ride-Sharing Platform, and Location Data-

본고의 목적은 국내 온라인 게임 산업, 라이드 셰어링 플랫폼 서비스 그리고 구글의 위치정보 반출 요청 사례를 통해 디지털 보호무역주의의 양상을 고찰하는 것에 있다. 사례 분석을 통해 아직 학문적으로나 정책적으로 규정되어지지 않은 보호무역주의의 개념 규정에 기여하고자 한다. 그리고 본고는 디지털 보호무역주의를 설명하기 위한 과정에서 국제정치경제학의 이론적 발전도 꾀할 수 있을 것으로 기대한다. 본고는 세서비스 산업 사례―온라인 게임 서비스 산업, 모빌리티 서비스 산업, 위치정보 제공 서비스 산업―를 디지털보호무역주의의 탐색적 관점에서 선정한다. 사례 분석을 통해, 디지털 보호무역주의에서의 새로운 행위자의 등장, 행위자 다양성 증대, 사회문화적 요인의 중요성, 인과관계의 복합성이 발견되었으며, 이는 국제정치경제학 이론의 재접근 혹은 새로운 관점 개발의 필요성을 부여하고 있다.This study aims to examine cases on digital protectionism in South Korea. Due to the ever increasing size of digital economy, digital trade in national economies is gaining more and more importance. And yet, digital protectionism has been barely discussed among scholars of (international) political economy. The communities of policy-makers and the field of international political economy alike lacks consensus as to the definition of digital protectionism, let alone significant efforts to when, how, and when digital protectionism emerges. Three cases are particularly highlighted to fill the gap, including the online game service industry, ride-sharing platform service industry, and location-based data service industry. The case analysis is purposed to be exploratory and thus does not intend to systematically test theoretical propositions made in the present study. Findings from the case analysis pose challenges to or call for the revisitation on theories in the field of international political economy because of new actors, factors, and added causal complexities observed in the cases of digital protectionism.

Allocation Problems in Ride-Sharing Platforms: Online Matching With Offline Reusable Resources

Bipartite matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this paper, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based adaptive algorithm that achieves an online competitive ratio of 1/2 − ε for any given ε > 0. We also show that no non-adaptive algorithm can achieve a ratio of 1/2 + o(1) based on the same benchmark LP. Through a data-driven analysis on a massive openly-available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ride-sharing systems. We also present heuristics that perform well in practice.

Frustration-Based Promotions: Field Experiments in Ride-Sharing

The service industry has become increasingly competitive. One of the main drivers for increasing profits and market share is service quality. When a consumer encounters a bad experience, or a frustration, s/he may be tempted to stop using the service. In collaboration with the ride-sharing platform Via, our goal is to understand the benefits of proactively compensating customers who have experienced a frustration. Motivated by historical data, we consider two types of frustrations: long waiting times and long travel times. We design and run three field experiments to investigate how different types of compensation affect the engagement of riders who experienced a frustration. We find that sending proactive compensation to frustrated riders (i) is profitable and boosts their engagement behavior, (ii) works well for long waiting times but not for long travel times, (iii) seems more effective than sending the same offer to non-frustrated riders, and (iv) has an impact moderated by past usage frequency. We also observe that the best strategy is to send credit for future usage (as opposed to waiving the charge or sending an apologetic message).

Beyond solving the last mile problem: the substitution effects of bike-sharing on a ride-sharing platform

ABSTRACTRide-sharing services have been popularly used for both short-distance and long-distance trips. The recent introduction and growth of bike-sharing services can help solve the last mile problem and provide a good mobility option, especially for short-distance travellers. We examine the extent to which bike-sharing platforms solve the last mile problem and affect short-distance travels by studying their impacts on ride-sharing orders. To do so, we exploit a natural experiment setting in which bike-sharing platforms were newly launched in Chengdu, China. We combine detailed granular one-month order data collected from DiDi with Chengdu’s geographic information data from Gaode Map, a Chinese leading map provider. We find that the introduction of bike-sharing platforms significantly reduces short-distance ride-sharing orders. We additionally examine the heterogeneity in substitution effects across different pickup and drop-off locations and find that location attributes significantly affect bike-sharing’s substitution effects on ride-sharing platforms.

When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms

Providing transparency into operational processes can change consumer and worker behavior. However, it is unclear whether operational transparency is beneficial with potentially biased service providers. We explore this in the context of ridesharing platforms where early evidence documents bias similar to what has been observed in traditional transportation systems. Platforms responded by reducing operational transparency through removing information about riders' gender and race from the ride request presented to drivers. However, following this change, bias may still manifest through driver cancelation after a request is accepted, at which point the rider's picture is displayed. Our primary research question is to what extent a rider's gender, race, and perception of support for lesbian, gay, bisexual, and transgender (LGBT) rights impact cancelation rates. We investigate this through a large field experiment on a major ridesharing platform in Washington, DC. By manipulating rider names and profile pictures, we observe drivers' behavior patterns in accepting and canceling rides. Our results confirm that bias at the ride request stage has been eliminated. However, after acceptance, racial and LGBT biases are persistent, while we find no evidence of gender biases. We also explore whether peak times moderate (through increased pay to drivers) or exacerbate (by signaling that there are many riders, allowing drivers to be more selective) these biases. We find a moderating effect of peak timing, with lower cancelation rates for non-caucasian riders. We do not find a similar moderating effect for riders that signal support for the LGBT community.

Ride-sharing platforms from drivers’ perspective: Evidence from Uber and Lyft drivers

Ride-sharing platforms from drivers’ perspective: Evidence from Uber and Lyft drivers

Taxi, Ride-Sourcing and Ride-Sharing Services – Background Note by the Secretariat

Traditional taxi services – together with other providers – constitute a crucial part of the urban transportation environment. Over the last 10 years the emergence of ride-sourcing and ride-sharing platforms put under competitive pressure the activity of traditional taxis. While these new services provide clear benefits to consumers, they largely operate outside of any regulatory framework, raising new questions for regulators and competition law enforcers. This note looks at the rules that apply to traditional taxis which might need reviewing in light of digitalisation. Technological developments have affected several aspects of the pre-booked and street-hailing segment, perhaps removing the need for some common regulations. Regulators have the opportunity to review existing rules, softening (or abolishing) those whose original justifications are not valid anymore. However, technological development may not solve some of the market failures affecting the sector. Thus, while some changes to the regulation of traditional taxis would help to establish a level playing field and increase competition in the market, some rules might need to be applied to new providers as well. Competition between traditional taxis and new services has also been at the top of National Competition Authorities’ (NCA) agenda. These have exerted a significant effort to enrich the discussion thorough their advocacy activity. A common point of view emerges from their work, as all the NCAs highlight the priority of recognising – and not banning – the new providers in order to increase competition in the market. The paper also analyses the role of new providers from a competition law enforcement perspective. Although competition law cases regarding ride-sourcing and ride-sharing services are very limited in number, growing volume of such services, their two-sided nature and issues related to big data pose potential challenges to competition authorities. This note discusses some of the issues NCAs might face in the future.

Joint Pricing and Matching in Ridesharing Systems

Ride-sharing firms use pricing and matching decisions to control the ride-sharing platforms. Those decisions can be made jointly or interchangeably, which raises the following questions: Is matching optimization necessary? Specifically, is fixing the matching decisions to a simple rule and optimizing only the pricing decisions enough to achieve the optimal performance? In order to answer these questions, we study the interplay between pricing and matching decisions of a ride-sharing firm. There are many studies in the ride-sharing literature that optimize the pricing decisions under an assumed matching policy. However, we show that ignoring matching optimization can result in subpar overall performance. We formulate a stylized ride-sharing model that captures customer and driver behaviors and geospatial nature of the system. Customers are both price and delay sensitive, and drivers are strategic and self-scheduling. We prove that optimizing the matching decisions have first-order effect on the system performance. We show that fixing the matching decisions and optimizing only the pricing decisions does not maximize the number of matchings in general. Similarly, we show that fixing the pricing decisions and optimizing only the matching decisions is not optimal in general. Finally, we show that optimizing in only one dimension (either pricing or matching) has no benefit to the firm under some conditions, whereas joint pricing and matching optimization can lead to a significant performance increase.

Competition Between Two-Sided Platforms Under Demand and Supply Congestion Effects

Problem definition: This paper explores the impact of competition between platforms in the sharing economy. Examples include the cases of Uber and Lyft in the context of ride-sharing platforms. In particular, we consider competition between two platforms that offer a common service (e.g., rides) through a set of independent service providers (e.g., drivers) to a market of customers. Each platform sets a price that is charged to customers for obtaining service provided by a driver. A portion of that price is paid to the driver that delivers the service. Both customers’ and drivers’ utilities are sensitive to the payment terms set by the platform and are also sensitive to congestion in the system (given by the relative number of customers and drivers in the market). We consider two possible settings. The first one, termed “single-homing,” assumes that drivers work through a single platform. In the second setting, termed “multi-homing” (or “multi-apping” as it is known in practice), drivers deliver service through both platforms. Academic/Practical Relevance: This is one of the first papers to study competition and multi-homing in the presence of congestion effects typically observed in the sharing economy. We leverage the model to study some practical questions that have received significant press attention (and stirred some controversies) in the ride-sharing industry. The first involves the issue of surge pricing. The second involves the increasingly common practice of drivers choosing to operate on multiple platforms (multi-homing). Methodology: We formulate our problem as a pricing game between two platforms and employ the concept of Nash equilibrium to analyze equilibrium outcomes in various settings. Results: In both the single-homing and multi-homing settings, we study the equilibrium prices that emerge from the competitive interaction between the platforms and explore the supply and demand outcomes that can arise at equilibrium. We build on these equilibrium results to study the impact of surge pricing in response to a surge in demand and to examine the incentives at play when drivers engage in multi-homing. Managerial Implications: We find that raising prices in response to a surge in demand makes drivers and customers better off than if platforms were constrained to charge the same prices that would arise under normal demand levels. We also compare drivers’ and customers’ performance when all drivers either single-home or all multi-home. We find that, while individual drivers may have an incentive to multi-home, all players are worse off when all drivers multi-home. We conclude by proposing an incentive mechanism to discourage multi-homing.

Impact of Bilateral Rating System on Ride-Sharing Platforms

Traditional online platforms (e.g., Amazon Marketplace) use Unilateral Rating System (URS), where customers rate sellers. However, sharing economy platforms (e.g., Uber, Airbnb) have adopted Bilateral Rating System (BRS) that also allows service providers to rate customers, and even selects customers based on their ratings. BRS is often purported to be better than URS, as BRS unlocks more hidden information than URS by revealing ratings of customers to service providers before they make acceptance/rejection decisions. We compare URS and BRS in the context of the ride-sharing service to study its impact on all stakeholders in the system. Conventional wisdom suggests that BRS would harm riders, as riders can be rejected by drivers while platforms should benefit from BRS, since it unlocks the value in the riders’ rating that can be potentially extracted. We find that contrary to this intuition, BRS may increase riders’ welfare under some conditions, as drivers rejecting low-rating riders can reduce the excess demand and the associated inconvenience cost born by all riders in the system. BRS can both improve or reduce the platform’s revenue, as there is a tug-of-war between the platform and drivers on the revenue share. Under the right conditions, BRS can result in a win-win-win situation for the drivers, riders and the platform.