Ride-hailing Platforms Research Articles

Impact of privacy regulation involving information collection on the ride-hailing market

Ride-hailing platforms can indirectly adjust privacy-sensitive customers’ demand via pricing and information collection strategies while encouraging drivers to provide services by offering an attractive wage rate. Since the privacy information collected by platforms brings some negative effects, governments enact privacy regulations to protect customer privacy. Inspired by this observation, our paper aims to investigate the impact of privacy regulations on different stakeholders in the ride-hailing market, including customers, drivers, platforms, and regulators. Specifically, we formulate two multi-stage Stackelberg models under the scenarios with/without privacy regulation, respectively, compare the equilibrium results under these two scenarios, and obtain several interesting findings. First, the partial-coverage pricing strategy is more likely to be adopted by platforms than the full-coverage pricing strategy as privacy regulations are enforced. Second, when customers are not too severely disturbed by advertising, the privacy regulation transfers information decision-making right from the ride-hailing platform to customers and utilizes customers’ self-interest to alleviate the negative impact of information collected by the platform; Otherwise, it seems somewhat “redundant”. Third, effective regulation improves information security at the expense of benefits of stakeholders (the customer, drivers, and platforms). Therefore, the decision-making about privacy regulation results from a trade-off between economic benefits and information security, suggesting that regulators should enforce privacy regulation only if the level of externalities is high enough. This meaningful criterion can provide theoretical support for the decision-making of regulators.

Evolution of ride-hailing platforms regulations in India: a multi-level perspective

PurposeDigital platforms (DP) are transforming service delivery and affecting associated actors. The position of DPs is impacted by the regulations. However, emerging economies often lack the regulatory environment to support DPs. This paper aims to explore the regulatory developments for DPs using the multi-level perspective (MLP).Design/methodology/approachThe paper explores regulatory developments of ride-hailing platforms (RHPs) in India and their impacts. This study uses qualitative interview data from platform representatives, bureaucrats, drivers, experts and policy documents.FindingsRegulatory developments in the ride-hailing space cannot be explained as a linear progression. The static institutional assumptions, especially without considering the multi-actors and multi-levels in policy formulation, do not serve associated actors adequately in different times and spaces. The RHPs regulations must consider the perspective of new RHPs and the support available to them. Non-consideration of short- and long-term perspectives of RHPs may have unequal outcomes for established and new RHPs.Research limitations/implicationsThis research has implications for the digital economy regulatory ecosystem, DPs and implications for policymakers. Though the data from legal documents and qualitative interviews is adequate, transactional data from the RHPs and interviews with judiciary actors would have been insightful.Practical implicationsThe study provides insights into critical aspects of regulatory evolution, governance and regulatory impact on the DPs’ ecosystem. The right balance of regulations according to the business models of DPs allows DPs to have space for growth and development of the platform ecosystem.Social implicationsThis research shows the interactions in the digital space and how regulations can impact various actors. A balanced policy can guide the paths of DPs to have equal opportunities.Originality/valueDP regulations have a complex structure. The paper studies regulatory developments of DPs and the impacts of governance and controls on associated players and platform ecosystems.

Predicting ride-hailing passenger demand: A POI-based adaptive clustering federated learning approach

Passenger demand prediction is a key task for online ride-hailing platforms to optimize their resource allocation and service quality. However, centralized data collection and mining of massive passengers’ travel data expose serious privacy and security risks. To address this challenge, we propose a POI-based Adaptive Clustering Federated Learning with Spatio-Temporal Graph Attention Gate Recurrent Unit Networks (CFL-STG) algorithm that achieves accurate passenger demand prediction while protecting passenger privacy. Firstly, we propose a localized data storage mechanism for preserving data privacy and facilitating federated learning among different regions. Then, to tackle the statistical heterogeneity of data in federated learning, we introduce a clustering strategy that groups the regions based on their Point of Interest (POI) features, which reflect their demand patterns. For each group, we design a Spatio-Temporal Graph Attention Gate Recurrent Unit Network (STG) as a local model that can capture the spatio-temporal dynamics and inter-regional influence of POI features on passenger demand. Experiments on real data show that the proposed framework outperforms the existing federated learning models and common centralized data mining methods in terms of demand prediction performance and learning efficiency.

Pricing Decisions for Competitive Ride-Hailing Platforms with the Combination of Inner-Group and Inter-Group Network Externalities

Based on two-sided market theory, this paper has studied the pricing problem of ride-hailing platforms with a combination of inter-group network externality and inner-group network externality. Two scenarios of user structure are considered. In scenario 1, both travellers and drivers are single-homing. In scenario 2, travellers are single-homing while drivers are multi-homing. Moreover, time sensitive factors and driver’s commission rate are introduced to reflect the characteristics of transport industry. Finally, the impact of network externality, time sensitivity, driver’s commission rate and entry cost on ride-hailing platform pricing, user scale and profits are analysed. The results show that inter-group network externality and inner-group network externality have a negative effect on platform prices charged to both travellers and drivers. However, when travellers are multi-homing, the price charged to travellers is positive with respect to the inter-group network externality from drivers. In the relationship between travellers’ scale and inter-group network externality, inner-group network externality is positive. Further, in both scenarios, the network externalities from the two sides affect platform profits negatively.

Governing work through personal data: The case of Uber drivers in Geneva

This article presents an ethnographic account of the advocacy initiative, conducted by NGO PersonalData.IO and the company Hestia.ai, that seeks to empower gig workers by helping them regain access to their personal data through data access rights, using the European Union General Data Protection Regulation. It is based on a case study of Uber drivers in Geneva that has a worldwide relevance for the gig economy. Previously self-employed, drivers are now classified as employees and their working time and earnings must be calculated according to local labour laws. We contribute to debates on algorithmic management in ride-hailing platforms by focusing on participatory methods of accountability through personal data, from an infrastructural perspective. First, we focus on the nexus between personal data protection and algorithmic management to understand the domination of ride-hailing platforms over the workers’ means of production, i.e., their personal data. We provide empirical transparency on the data structures of Uber for the sake of algorithmic accountability. These structures are utilised for their surge pricing algorithms and ultimately govern the workforce. Second, within a collective process of governance, we built participatory tools and methods for empowering gig workers and data scientists. These are means for calculating earnings and working that made explicit a new social meaning of work, i.e., “lost time between rides”.

On-Demand Ride-Hailing Service Platforms With Hired Drivers During Coronavirus (COVID-19) Outbreak: Can Blockchain Help?

During the recent COVID-19 (CoV) global outbreak, there is a sharp decline of revenue of on-demand ride-hailing (ODR) platforms because people have serious worries of infection in the shared vehicles. Blockchain, which supports cryptocurrency and creates full traceability of the service history of each car and driver, may come to rescue by allowing the platform to offer only the “safe cars” to consumers. Motivated by the real world challenges associated with the CoV outbreak for the ODR platform, we build game-theoretical models based on the M/M/n queuing system to explore if and how blockchain can help. In the basic model, the ODR platform decides the service price and special hygiene level. Comparing between the cases with and without blockchain, we find that blockchain implementation increases both the service price and hygiene level. In addition, when the consumers’ inherent worry of infection is substantially large, implementing blockchain achieves all-win for the ODR platform, drivers and consumers. In the extended models, we first consider the case when the special hygiene level is determined by the drivers under a mixed-leadership game and then explore the case when customers are risk averse. The main findings about blockchain adoption remain valid in both cases. However, when the drivers take charge of the special hygiene level, both optimal decisions are lower in most cases. It is also important to make efforts to reduce consumers’ feeling volatility toward service valuation for improving the value of blockchain adoption and related performances.

Stackelberg Pricing Game for Ride-Hailing Platforms With Combined Travel Modes

Stackelberg Pricing Game for Ride-Hailing Platforms With Combined Travel Modes

Platforms, Politics, and Policy Change: Framing A Governance Approach to TNCs in the Gig Economy

In social science, platform capitalism is becoming more analyzed as digital app-based platforms have revolutionized the global exchange of goods and services, prompting multifaceted challenges within society, economics, and politics. Platform governance emerges as this changing landscape unfolds, illuminating the intricate interplay between national and local regulatory frameworks and the burgeoning power dynamics among diverse stakeholders. This article provides a framework for examining the complex political and governance issues that have surfaced in the gig economy, mainly focused on the proliferation of ride-hailing platforms, such as Uber, also known as Transportation Network Companies (TNCs). This article examines the framework of platform governance contributing theoretically by putting into dialogue a literature primarily focused on studying social media with another branch of the platform society in which policy disruption has been evident, such as transportation and urban mobility. The analysis focuses on three fundamental dimensions of political economy: ideas, interests, and institutions. The paper also reviews how these three approaches have operated in recent scholarly literature. Finally, the article underscores the need for future research to address this complexity from a contingent approach to promote a comprehensive understanding of platform regulation.

Strategic driver repositioning in ride-hailing networks with dual sourcing

In ride-hailing markets, the spatial mismatch between supply and demand characterizes one of the most fundamental operational bottlenecks. The voluntary customer search of drivers is oftentimes inadequate for narrowing the demand-supply gaps and falls short in responding timely to the dynamic market conditions. To facilitate better supply management, some ride-hailing platforms now adopt a dual-sourcing strategy by converting some freelance drivers as contractors, who would then follow platforms’ guidance on repositioning movements when idle. This paper thus aims to analyze the implications of such a dual-sourcing strategy on the overall operations of ride-hailing services. We consider a monopolistic platform that owns a blended workforce, with freelance drivers searching for customers following their own preferences whilst contracted drivers repositioning in a centralized manner as system actuators. A mixed network equilibrium model is devised to capture the interplay between the idle repositioning movements of both parties. The theoretical existence of the mixed equilibrium is proved, and real-world data is invited to quantify the effects of dual sourcing on various market sectors. Our numerical results confirm the effectiveness of dual sourcing in smoothing operations, increasing customer demand, and driving up the revenue of ride-hailing services. However, the benefit of dual sourcing to the platform remains sensitive to the proportion of contractors in the entire workforce as well as the stimulative commission beyond their regular service income.

Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation Techniques

In recent years, ride-hailing services have emerged as a popular means of transportation for the residents of urban areas. There is an inequality in the spatio-temporal distribution of demand and supply, which requires the proper recommendation of routes to drivers in order to guide them towards riders optimally. This paper provides a review of different route recommendation strategies that have been applied in ride-hailing platforms with the main focus on fairness, and environmental issues. It is important to consider the environmental aspects of route recommendation systems as the transportation sector is one of the major sources of air pollution and has reduced the life expectancy of people around the globe. Moreover, there is an unfair distribution of resources and opportunities among the drivers and riders of the platform which has affected their long-term sustainability in the market. In this paper, we highlight the critical challenges and opportunities inherent in the design of green and fair route recommendation systems and indicate some possible directions for future research.

GNN-based passenger request prediction

ABSTRACT Passenger request prediction is essential for operations planning, control, and management in ride-hailing platforms. While the demand prediction problem has been studied extensively, the Origin-Destination (OD) flow prediction of passengers has received less attention from the research community. This paper develops a Graph Neural Network (GNN) framework along with the Attention Mechanism to predict the OD flow of passengers. The proposed framework exploits various linear and non-linear dependencies that arise among requests originating from different locations and captures the repetition pattern and the contextual data of that place. Moreover, the optimal size of the grid cell that covers the road network and preserves the complexity and accuracy of the model is determined. Extensive simulations are conducted to examine the characteristics of our proposed approach and its various components. The results show the superior performance of our proposed model compared to the existing baselines.

Sharing Economy as Unconventional Alternative to Traditional Transport Services

Abstract The sharing economy, as a developing business model, is becoming more and more popular. This phenomenon can be observed in many countries. The sharing economy, as a part of human life in its essence, has existed for a long time. In general, the sharing economy is considered an economic model where individuals or organizations share a certain type of property or, in this way, provide services for a financial reward. One of the most used sectors, where there is constant interest, is the shared transport of people, goods, and services. However, platforms operating in the shared transportation segment should be analysed from various points of view, such as their financial results and overall economic stability. The study is primarily focused on the sector of shared transport and transport services. The chief objective of the study was to demonstrate the impact of the COVID-19 pandemic on shared transport and transport services within the territory of the Slovak Republic. The analysis included ride-hailing platforms such as Bolt Services SK as well as food and dining delivery platforms such as Wolt Slovakia. Through appropriately chosen methods of financial and economic analysis, the situation of the platforms before and during the COVID-19 pandemic was determined. We also performed an analysis of the development of the number of cars and the related level of automobilization in the territory of the Slovak Republic, since the shared economy represents an alternative to conventional economic models.

Implications of algorithmic management on careers and employment relationships in the gig economy – a developing country perspective

PurposeThis paper aims to explore the implications of algorithmic management on careers and employment relationships in the Nigerian gig economy. Specifically, drawing on labour process theory (LPT), this study provides an understanding of the production relations beyond the “traditional standard” to “nonstandard” forms of employment in a gig economy mediated by digital platforms or digital forms of work, especially on ride-hailing platforms (Uber and Bolt).Design/methodology/approachThis study adopted the interpretive qualitative approach and a semi-structured interview of 49 participants, including 46 platform drivers and 3 platform managers from Uber and Bolt.FindingsThis study addresses the theoretical underpinnings of the LPT as it relates to algorithmic management and control in the digital platform economy. The study revealed that, despite the ultra-precarious working conditions and persistent uncertainty in employment relations under algorithmic management, the underlying key factors that motivate workers to engage in digital platform work include higher job flexibility and autonomy, as well as having a source of income. This study captured the human-digital interface and labour processes related to digital platform work in Nigeria. Findings of this study also revealed that algorithmic management enables a transactional exchange between platform providers and drivers, while relational exchanges occur between drivers and customers/passengers. Finally, this study highlighted the perceived impact of algorithmic management on the attitude and performance of workers.Originality/valueThe research presents an interesting case study to investigate the influence of algorithmic management and labour processes on employment relationships in the largest emerging economy in Africa.

From hand-held radio to ride-hailing platforms: Research on a local technical network of taxi drivers in China

This paper investigates how Chinese taxi drivers utilize low-tech equipment, such as handheld radios and mobile phone services, to form local ride-hailing networks. Previous studies have analyzed how Internet platforms like Didi Chuxing have reshaped labor relations through technology and policies, but they have ignored the fact that outside metropolitan areas, traditional technological solutions, such as on-call radio platforms, have not been replaced by the rapidly expanding ride-hailing platforms. Therefore, to outline China’s diverse ICT practice environment, this paper focuses on a case study in two Chinese towns (Laizhou, Shandong; Ziyang, Sichuan), shifting the attention from metropolitan areas to neglected fourth- and fifth-tier cities. After reviewing the previous advancements in mobile communication technology, we discuss the economic, cultural, and social motivations behind the use of ‘low-tech’ devices and cooperative services by the Chinese taxi drivers in question.

The impact of regulatory policy on the growth of ride-hailing platform: System dynamics perspective

Regulatory policy is an important factor affecting the development of emerging ride-hailing platforms. However, there is little research exploring the impact of specific regulatory policies on platforms from the perspective of enterprise growth. Taking the Chinese ride-hailing platform (RHP) as an example, this study employs the system dynamics method to simulate and analyze the impact of restrictive policies on the model of vehicles and the qualification of drivers on the growth of RHP. Furthermore, it compares and evaluates the effects of different countermeasures taken by the platform in the face of policy uncertainty. Specifically, this study dynamically examines the growth of platforms under loose/strict policy and short/long term policy scenarios, and evaluates the coping strategy of self-built fleet adopted by platform. The results show that regulatory policy uncertainty brings great challenges to the overall development of the RHP. A Self-built fleet not only has the potential to improve RHP's ability to resist policy risks but also increases the platform's scale effect at both ends of supply and demand. It is worth noting that RHP should devise the most suitable strategy according to a comprehensive evaluation of the characteristics of the regulatory policies and the cost-effectiveness of the countermeasure. It also shows that the promotion and application of new energy vehicles and automated vehicles can bring good profit margins to RHP in the future. This study can help policymakers and managers better understand of the impact of regulatory policy on the growth of RHP and also provide a valuable reference for promoting the healthy and orderly development of ride-hailing market.

Varieties of platform unionism: a view from the Global South on workers’ power in the digital economy

Nearly three decades ago, Manuel Castells declared the atomising effects of the new technologies of the ‘information age’ to presage the ‘end of labour’. There is little doubt that the labour movement worldwide is no longer the social force it was in the twentieth century. Much of the debate on the future of work and consequences for worker organisation, moreover, has focused on defensive struggles against the introduction of new technologies in the Global North. Technological change has also led, however, to struggles in the Global South. These ‘technological fixes’ have historically contributed to the ‘remaking’ of new working classes and related ‘offensive’ struggles, the latest of which is digitalisation and algorithmic management. In this primarily conceptual article, we adopt a power resources approach to an analysis of these changes, using as our basis, a project encompassing eight empirical case studies on recent labour organising in on-location platform economies of both the Global North and South. Analysis of food-delivery and private ride-hailing platforms in Argentina and Uganda, respectively, showed different varieties of platform unionism, with forms of worker organisation in the Global South tending to more autonomy and hybridity. In some cases, these self-organised worker collectives go beyond established forms of unionism in attempts to control the platform technologies. We conclude by suggesting that the experiments of platform workers with new forms of power and organisation, particularly in the Global South, are important to follow in the Global North.

Control of the Platform Reserve Army: The Roles of the State and Capital in China's Platform Economy

The rise of digital platforms has formed a platform reserve army. The platform reserve army is the stagnant reserve army of contemporary digital capitalism, which brings challenges to labor control. Using data collected from an original survey of over 600 ride-hailing drivers in two Chinese cities, the roles of the state and capital, as well as their interactions in shaping capital's control over the platform reserve army in China, are clarified. Against the backdrop of the state's market-formalization policy, ride-hailing platforms have aligned with third-party rental companies that provide qualified vehicles. The platform-rental company alliance has effectively trapped a significant share of drivers to maintain an adequate and stable labor supply. Given their capacity to manipulate and restructure the reserve army, platforms have capitalized on the state's regulations to strengthen their control over labor.

Bilateral Pricing of Ride-Hailing Platforms Considering Cross-Group Network Effect and Congestion Effect

The pricing of ride-hailing platforms (e.g., Didi Rider and Uber) is heavily and simultaneously influenced by the cross-group network effect and congestion effect. To analyze the bilateral pricing of ride-hailing platforms under the influence of these two effects, in this paper we construct a game-theoretic model under four different scenarios and analyze the equilibrium outcomes. The results show that: (1) when both passengers and drivers are sensitive to hassle costs, if the cross-group network effect on the passenger side is higher than that on the driver side, then the platform’s pricing on both sides increases with the increase in the congestion effect, otherwise the prices on both sides of the platform decrease with the increase in the congestion effect; (2) when passengers are sensitive to hassle costs and drivers are sensitive to price, if the ratio for passengers’ and drivers’ different perceptions of price and hassle cost is greater than a certain threshold, then the platform’s pricing on the passenger side increases with the increase in the congestion effect and the platform’s pricing on the driver side decreases with the increase in the congestion effect, otherwise the platform’s pricing on the passenger side decreases with the increase in the congestion effect and the platform’s pricing on the drivers’ side increases with the increase in the congestion effect; (3) when passengers are sensitive to price and drivers are sensitive to hassle costs, if the ratio for passengers’ and drivers’ different perceptions of price and hassle costs is greater than a certain threshold, then the platform’s pricing on the passenger side decreases with the increase in the congestion effect and the platform’s pricing on the drivers’ side increases with the increase in the congestion effect, otherwise the platform’s pricing on the passenger side increases with the increase of the congestion effect and the platform’s pricing on the driver side decreases with the increase in the congestion effect; (4) when both passengers and drivers are price-sensitive, if the cross-group network effect on the passengers’ side is larger than that on the drivers’ side, then the platform should decrease its pricing on both sides with the increase in the congestion effect, otherwise, if the cross-group network effect on the passengers’ side is less than that on the drivers’ side, the platform should increase its pricing on both sides with the increase in the congestion effect; (5) the platform is able to generate the highest profit in each scenario, and the results of the profit comparison between the four scenarios depends on the cross-group network effects and the congestion effects on both the passengers’ and the drivers’ sides.

Comparison of three participation modes on ride-hailing platforms

With the rapid development of ride-hailing services, various participation modes have emerged on ride-hailing platforms that are recruiting drivers to serve the growing number of passengers. This paper investigates three popular participation modes for a person joining the ride-hailing platform as a driver: car buying mode (BM), short-term leasing mode (SLM), and financial leasing mode (FLM). We compare these three modes and study which is the best choice from the perspective of the driver and platform, considering different types of drivers (myopic or strategic). We develop an analytical model to explore this problem. Interestingly, even when the driver bears a high cost of maintenance, BM always dominates the other two modes for the driver. We find that the platform’s optimal strategy depends on the driver type. FLM will never be an optimal choice for the platform if the driver is strategic. However, if the driver is myopic, FLM will be the best mode under certain conditions. We also find that when the maintenance cost is moderate, regardless of the driver type, the driver and platform can realise a win-win situation with SLM, but otherwise, they are better off adopting BM. Moreover, with moderate maintenance cost, FLM can be a win-win strategy if the driver is myopic. Lastly, our study discloses that increasing the new-driver rewards benefits both the driver and platform only if the driver is myopic and the reward rate exceeds a certain threshold.

How to Survive in the Shadow of Sharing Economy Giants: Business Model Innovation for Small and Medium-Sized Platforms

This study aims to explore how small and medium-sized platforms in the sharing economy can gain a competitive advantage in a market monopolized by giants. Taking China’s ride-hailing industry as an example, the k-means clustering method was employed to compare and analyze the characteristics and differences of 207 small and medium-sized platforms based on the framework of the business model canvas. An empirical typology comprising four representative successful business models of small and medium-sized ride-hailing platforms is extracted from the dimensions of key partners, value propositions, customer segmentation, and cost-revenue structure: high-end business platforms, Minibus-hailing platforms, Intercity carpool platforms, and Aggregation mode platforms. The results show that small and medium-sized platforms in the sharing economy can rely on their own characteristics and advantages to seek new potential development directions and gain development space by constructing value propositions and operating models that are different from industry giants. Specifically, it is necessary to further implement refined market identification and segmentation to continuously develop differentiated competitive advantages. In particular, more effective and open business strategies should be explored and practiced to greatly expand the network effects and business boundaries of small and medium-sized platforms, thereby significantly enhancing their competitiveness in the ride-hailing market. This study provides a new perspective on the reconstruction of market exchange in the sharing economy through the prism of small and medium-sized digital platforms and has certain important theoretical significance for supplementing and advancing existing research.