Ride-hailing Platform Research Articles

Regulating TNCs: Should Uber and Lyft set their own rules?

We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) A minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings can push the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard competitive labor market theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floor curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. We also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles.

Compensation Decisions on Disruption Recovery Service in Urban Rail Transit

Ride-hailing, in addition to a common mode of daily transportation, is an attractive option for evacuating stranded passengers and supplementing bus bridging in the early stages of an urban rail transit (URT) disruption. This paper proposes a service supply chain comprised of ride-hailing vehicles, ride-hailing platforms, and stranded passengers wherein the URT and ride-hailing chain together provide emergency evacuation services. The emergency evacuation service supply chain can be coordinated under an effort-based revenue sharing contract. A URT-dominated Stackelberg game model between the URT and ride-hailing platform is then formulated to optimize compensation decisions on the part of the URT; numerical analysis reveals critical factors affecting the said decisions. The main contributions of this paper are two-fold: first, it provides new information regarding collaboration between URT operators and ride-hailing platforms for stranded passenger evacuation, including a ride-hailing platform pricing strategy; and second, the URT compensation decision process is solved via Stackelberg game model while revealing an incentive coefficient parameter for the URT decision and solver.

How app-based ride-hailing services influence travel behavior: An empirical study from China

App-based ride-hailing is a newly emergent mode of travel that is expected to influence passengers’ travel behavior significantly. In this study, we define app-based ride-hailing services to include hailing of taxis through smartphone apps and sharing of private vehicles (car sharing is not included). This paper is one of the first quantitative studies to examine how app-based ride hailing would impact passengers’ choice of travel mode and change car-purchasing behaviors. China is taken as the empirical context, and the data are from a large-scale app-based survey conducted via the largest app-based ride-hailing platform in the world. Drawing on these rich survey data, the investigation covers both the short-term travel mode choices and long-term car-purchasing behaviors of app-based ride-hailing users. The research findings are helpful in understanding changes in urban residents’ travel behaviors under in context of app-based ride hailing, and provide valuable in-depth insights for both governments and app-based ride-hailing service providers (i.e., enterprises) for the management and regulation of ride hailing.

Evolutionary Game between Government and Ride-Hailing Platform: Evidence from China

The ride-hailing industry is a new business form that combines traditional taxi services with Internet technology and a sharing economy. However, after its emergence, countries have focused on finding ways to regulate this industry. The regulation of ride-hailing has gone through three stages: from denial of negation to laissez-faire and prudential supervision. This study focuses on the market regulation of the ride-hailing industry, discusses whether ride-hailing platforms require strict regulation under the current Internet setting, and provides evidence for this problem from the perspective of evolutionary game theory between the behavior of the government and the platforms. This study argues that both ride-hailing platforms and the government are evolutionary game players with bounded rationality, constantly adjusting their strategies through confrontation, dependence, and restriction. Therefore, this study constructs a two-dimensional game model between the government and ride-hailing platforms and analyzes the stability strategies of the two participants in different scenarios, to clarify the game behavior and the game return matrix. Assuming that loose government regulation and the standard operation of the ride-hailing platforms are the optimal Pareto equilibrium of the game system, the study concludes that this optimal equilibrium cannot be achieved under the current conditions. Through parameter analysis and sample simulation calculations, the system can be directed toward this equilibrium by reducing government supervision cost and increasing government punishment. This provides a theoretical basis for the government to regulate the ride-hailing industry from the perspective of quantitative analysis. Related implications are finally proposed, which can help the decision-makers better understand the regulation countermeasures of the government and ride-hailing platforms.

Research on compatibility strategy of ride-hailing platform

In this paper, the compatibility strategy of ride-hailing platforms is analysed based on two-sided market theory. The basic model is constructed by considering the utility function of travellers and drivers. Then two situations of compatibility model are extended based on the basic model. In situation 1, the cross-platform cost is afforded by travellers. And in situation 2, the cross-platform cost is afforded by platforms. The equilibrium price and profit in both situations of compatibility model are analysed. Moreover, the factors of average waiting time and driver's commission rate are also introduced in order to reflect the characteristic of the ride-hailing service. Finally, the results from the basic model and compatibility model with two situations are analysed and compared. The main results indicate that the equilibrium price charged to travellers in situation 1 is always higher than that in situation 2, while the price charged to drivers in situation 1 is always lower than that in situation 2. Moreover, the ride-hailing platforms that afford the cross-platform cost can earn more profit. And the relationship between driver's commission rate, average cost per transaction and equilibrium price, total profit is also depending on the probability that travellers choose to make cross-platform transactions.

基于大学生视角的网约车平台服务现状分析

基于大学生视角的网约车平台服务现状分析

Harnessing the Double-Edged Sword via Routing: Information Provision on Ride-Hailing Platforms

We consider a ride-hailing platform that provides free information to taxi drivers. Upon receiving a rider's request, the platform broadcasts the rider's origin and destination to idle drivers, who accept or ignore the request depending on the profitability considerations. We show that providing such information may reduce drivers' equilibrium profit. Hence information provision is a double-edged sword: the drivers may choose to take more profitable riders via strategic idling. When multiple drivers compete for the same request, how the platform breaks the tie affects the incentives of the drivers. We propose a routing policy that can align the incentives and achieve the first-best outcome for large systems.

The Impact of E-Hailing Competition on the Urban Taxi Ecosystem and Governance Strategy from a Rent-Seeking Perspective: The China E-Hailing Platform

Nowadays, the way of Internet travel in China presents diversified development. Rapid expansion in the personal mobile terminal and Internet infrastructure for the Internet travels to the ecosystem of development and evolution. This paper explores the influence of the expansion of the e-hailing ecological community on the urban taxi ecological community, and the problems of profit-seeking, rent-setting, and rent-seeking from the capital behind the e-hailing platform. With entrance into the urban travel ecosystem with ride-hailing platform enterprises and emerging business models, how does (venture) capital cause the change of the urban taxi ecology? Is it essentially the rent-setting and rent-seeking behavior of capital that obtains the market monopoly status of e-hailing? What are the difficulties for governments and the public in regulating rent-seeking in capital and emerging business models? The aim is to explore the new governing strategy of urban taxi ecology. The paper chooses the case of the DiDi e-hailing platform, and performs a study on the utility and indifference curves quantitatively, and constructs a rent-seeking liaison between platform’s entry into the market and the platform’s monopoly. In this paper, the ecological system of the taxi industry is first sorted out, and the community relationship between the traditional cruise taxi industry and the e-hailing industry, and their market supply and demand relationship are clarified. Secondly, from the main theoretical perspective of rent-seeking behavior, this paper analyzes the causes of the rent-setting rent-seeking behavior of e-hailing platform enterprises and the venture capital behind them, and the problems of public power supervision. As the red pack & subsidy market strategy changes the utility (indifference) curve of passengers, the consumption preference of passengers is more inclined to e-hailing. With the expansion of the e-hailing community, the e-hailing platform partly replaces the regulatory role of the government, and venture capital investors as rational-economic individuals, and its agency responsibilities and profit-seeking characteristics naturally form a contradictory pair. The introduction of competition is a means among market policies to break monopoly. Even so, commercial competitors are able to avoid laws or regulations by the means of commercial mergers or through the implementation of variable interest entities control. Governments should therefore seek to participate actively in the governance of such organizations.

An empirical investigation of taxi driver response behavior to ride-hailing requests: A spatio-temporal perspective.

Using data provided by a ride-hailing platform, this paper examines the factors that affect taxi driver response behavior to ride-hailing requests. The empirical investigation from a driver’s perspective is of great importance for ride-hailing service providers, given that approximately 40% of the hailing requests receive no response from any driver. To comprehensively understand taxi driver response behavior, we use a rich dataset to generate variables related to the spatio-temporal supply-demand intensities, the economic incentives, the requests’ and the drivers’ characteristics. The results show that drivers are more likely to respond to requests with economic incentives (especially a firm subsidy), and those with a lower spatio-temporal demand intensity or a higher spatio-temporal supply intensity. In addition, drivers are more likely to respond to requests involving rides covering a greater geographical distance and to those with a smaller number of repeated submissions. The drivers’ characteristics, namely, the number of requests received and the number of requests responded, however, have relatively little impacts on their response probability to the current request. Our findings contribute to the related literature and provide managerial implications for ride-hailing service providers.

Opportunities or threats: The rise of Online Collaborative Consumption (OCC) and its impact on new car sales

Abstract Online collaborative consumption models, such as Uber and Airbnb, have emerged as popular peer-to-peer platforms in the sharing economy. The recent introduction of ride-hailing apps for smartphones has generated a powerful medium for passengers to call cars effortlessly and flexible job opportunities for drivers. A central question surrounding the introduction of online collaborative consumption regards its impact on incumbent firms. For example, ride-hailing services could discourage private car ownership, potentially leading to a subsequent decline of new car sales. Our study investigates whether the adoption of a leading ride-hailing platform, Didi Chuxing, increases or decreases new car sales shortly after the platform’s entries across 51 cities in China. Our empirical results suggest that the initial entry of a dominant ride-hailing company like Didi Chuxing positively impacts new car sales in the short run. However, we suspect that this positive effect will be transitory. Whether the auto industry can leverage the ride-hailing platforms to achieve sustainable benefits remains to be seen in the long run.

Surge Pricing and Its Spatial Supply Response

We consider the pricing problem faced by a revenue-maximizing platform matching price-sensitive customers to flexible supply units within a geographic area. This can be interpreted as the problem faced in the short term by a ride-hailing platform. We propose a two-dimensional framework in which a platform selects prices for different locations and drivers respond by choosing where to relocate, in equilibrium, based on prices, travel costs, and driver congestion levels. The platform’s problem is an infinite-dimensional optimization problem with equilibrium constraints. We elucidate structural properties of supply equilibria and the corresponding utilities that emerge and establish a form of spatial decomposition, which allows us to localize the analysis to regions of movement. In turn, uncovering an appropriate knapsack structure to the platform’s problem, we establish a crisp local characterization of the optimal prices and the corresponding supply response. In the optimal solution, the platform applies different treatments to different locations. In some locations, prices are set so that supply and demand are perfectly matched; overcongestion is induced in other locations, and some less profitable locations are indirectly priced out. To obtain insights on the global structure of an optimal solution, we derive in quasi-closed form the optimal solution for a family of models characterized by a demand shock. The optimal solution, although better balancing supply and demand around the shock, quite interestingly also ends up inducing movement away from it. This paper was accepted by David Simchi-Levi, optimization.

Spatial Capacity Planning

We study the relationship between capacity and performance for a service firm with spatial operations, in the sense that requests arrive with origin-destination pairs. An example of such a system is a ride-hailing platform in which each customer arrives in the system with the need to travel from an origin to a destination. We propose a state-dependent queueing model that captures spatial frictions as well as spatial economies of scale through the service rate. In a classical M/M/n queueing model, the square root safety (SRS) staffing rule is known to balance server utilization and customer wait times. By contrast, we find that the SRS rule does not lead to such a balance in spatial systems. In a spatial environment, pickup times increase the load in the system; furthermore, they are an endogenous source of extra workload that leads the system to only operate efficiently if there is sufficient imbalance between supply and demand. In heavy traffic, we derive the mapping from load to regimes of interest and establish that to obtain a balance of utilization and wait times, the service firm should use a higher safety factor, proportional to the offered load to the power of 2/3. We discuss implications of these results for general systems.

Surge Pricing and Its Spatial Supply Response

We consider the pricing problem faced by a revenue maximizing platform matching price-sensitive customers to flexible supply units within a geographic area. This can be interpreted as the problem faced in the short-term by a ride-hailing platform. We propose a two-dimensional framework in which a platform selects prices for different locations, and drivers respond by choosing where to relocate in equilibrium based on prices, travel costs and driver congestion levels. The platform's problem is an infinite-dimensional optimization problem with equilibrium constraints. We elucidate structural properties of supply equilibria and the corresponding utilities that emerge and establish a form of spatial decomposition, which allows us to localize the analysis to regions of movement. In turn, uncovering an appropriate knapsack structure to the platform's problem, we establish a crisp local characterization of the optimal prices and the corresponding supply response. In the optimal solution the platform applies different treatments to different locations. In some locations, prices are set so that supply and demand are perfectly matched; over-congestion is induced in other locations, and some less profitable locations are indirectly priced out. To obtain insights on the global structure of an optimal solution, we derive in quasi-closed form the optimal solution for a family of models characterized by a demand shock. The optimal solution, while better balancing supply and demand around the shock, quite interestingly, also ends up inducing movement away from it.

The Impact of Behavioral and Economic Drivers on Gig Economy Workers

Problem Definition: Gig economy companies benefit from labor flexibility by hiring independent workers in response to real-time demand. However, workers’ flexibility in their work schedule poses a great challenge in terms of planning and committing to a service capacity. Understanding what motivates gig economy workers is thus of great importance. In collaboration with a ride-hailing platform, we study how on-demand workers make labor decisions; specifically, when to work and for how long. Academic/Practical Relevance: Our model offers a way to reconcile competing theories of labor supply regarding the impact of financial incentives and behavioral motives on labor decisions. We are interested in both improving how to predict the behavior of gig economy workers and understanding how to design better incentives. Methodology: Using a large comprehensive dataset, we develop an econometric model to analyze workers’ labor decisions and responses to incentives while accounting for sample selection and endogeneity. Results: We find that financial incentives have a significant positive influence on the decision to work and on the work duration—confirming the positive income elasticity posited by the standard income effect. We also find support for a behavioral theory as workers exhibit income-targeting behavior (working less when reaching an income goal) and inertia (working more after working for a longer period). Managerial Implications: We demonstrate via numerical experiments that incentive optimization based on our insights can increase service capacity by 22% without incurring additional cost, or maintain the same capacity at a 30% lower cost. Ignoring behavioral factors could lead to understaffing by 10–17% below the optimal capacity level. Lastly, inertia could be a potential sign of workers’ loyalty to the platform

Enhancing the development of sharing economy to mitigate the carbon emission: a case study of online ride-hailing development in China

The rapid development of online ride-hailing sharing economic platform provided an efficient way to mitigate the carbon emission of modern traffic in China. However, its development was blocked by the social management system whose update is relatively slow. How to resolve the conflict between the new economic paradigm development under the technology empowerment and the old public administration system and effectively solve the new economic and social problems it brought by are the major challenges for the healthy and sustainable development of this type of sharing economy. To shed light on the solutions to such conflict, the present paper conducts a case study on China’s largest online ride-hailing platform ‘Didi Chuxing’. The results indicate that the separation of ownership and use rights following the sharing economy brings a new approach to the efficient use of resources. The development of Didi displays a bottom-up policy innovation and institutional change. The path choice of enterprise development will affect its acquisition of legitimacy to a great extent. In this mobile Internet era, the public plays a critical role in shaping the new policy.

Coordinating Supply and Demand on an On-Demand Service Platform with Impatient Customers

We consider an on-demand service platform using earning-sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price-sensitive customers with heterogeneous valuation of the service. As such, the supply and demand are “endogenously” dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a queueing model in the customer utility function to characterize the optimal price and wage rates that maximize the profit of the platform. We first analyze a base model that uses a fixed payout ratio (i.e., the ratio of wage over price), and then extend our model to allow the platform to adopt a time-based payout ratio. We find that it is optimal for the platform to charge a higher price when demand increases; however, the optimal price is not necessarily monotonic when the provider capacity or the waiting cost increases. Furthermore, the platform should offer a higher payout ratio as demand increases, capacity decreases or customers become more sensitive to waiting time. We also find that the platform should lower its payout ratio as it grows with the number of providers and customer demand increasing at about the same rate. We use a set of actual data from a large on-demand ride-hailing platform to calibrate our model parameters in numerical experiments to illustrate some of our main insights.

The Regulatory Challenges of ‘Uberization’ in China: Classifying Ride-Hailing Drivers

There has been nascent litigation around the world on the employment classification of workers in the ‘sharing economy’, ‘gig economy’ or ‘on demand economy’. New business models and forms of work organization arising from rapid advancements in digital technologies (or ‘disruptive innovation’ as some have described the phenomenon) have sparked considerable debate on a wide array of regulatory issues, including the role of labour law protections for the emergent ‘digital workforce’. This debate is currently taking place in the People’s Republic of China. This article analyses how Chinese courts and lawmakers have addressed issues that are directly or indirectly relevant to determining the status of drivers in the ride-hailing sector. Despite the presence of criteria for identifying a ‘labour relationship’ under Chinese labour law, the courts have not adopted a uniform approach across different types of claims involving these drivers. A new government regulation on the ride-hailing sector, which took effect in November 2016, has not resolved this issue as it permits ride-hailing platform companies to enter into labour contracts and other forms of agreements with drivers based on the particularities of their work arrangements. In considering these recent cases and regulatory developments, the article concludes that a purposive approach to the existing criteria in Chinese labour law for ascertaining the status of workers in the sharing economy is useful for addressing the basic question of whether they should be protected, rather than creating ‘new’ categories of employment classification.