Ride-sharing Platform Research Articles

Sharing when stranger equals danger: Ridesharing during Covid-19 pandemic

Using data collected from one of the most popular city-to-city ridesharing platforms, we study how individual drivers respond to an increased health hazard. We analyze the impact of the Covid-19 outbreak on the level of mobility and the price of ridesharing. We show a higher level of prices and a larger price dispersion in areas with increased health risk. We exploit daily discontinuities in the reporting of Covid-19 cases to argue that the variation in prices is due to drivers’ perception of health risks. Our results suggest that a decision-maker promoting ridesharing as a solution to intercity travel should account for the impact of the perceived health risks on prices and find ways to ensure access.

A general equilibrium model for multi-passenger ridesharing systems with stable matching

This paper proposes a general equilibrium model for multi-passenger ridesharing systems, in which interactions between ridesharing drivers, passengers, platforms, and transportation networks are endogenously captured. Stable matching is modeled as an equilibrium problem in which no ridesharing driver or passenger can reduce his/her ridesharing disutility by unilaterally switching to another matching sequence.This paper is one of the first studies that explicitly integrates the ridesharing platform’s multi-passenger matching problem into the model. By integrating matching sequence with hyper-network, ridesharing-passenger transfers are avoided in a multi-passenger ridesharing system. Moreover, the matching stability between the ridesharing drivers and passengers is extended to address the multi-OD multi-passenger case in terms of matching sequence. The paper provides a proof for the existence of the proposed general equilibrium.A sequence-bush algorithm is developed for solving the multi-passenger ridesharing equilibrium problem. This algorithm is capable to handle complex ridesharing constraints implicitly. Results illustrate that the proposed sequence-bush algorithm outperforms general-purpose solver, and provides insights into the equilibrium of the joint stable matching and route choice problem. Numerical experiments indicate that ridesharing trips are typically longer than average trip lengths. Sensitivity analysis suggests that a properly designed ridesharing unit price is necessary to achieve network benefits, and travelers with relatively lower values of time are more likely to participate in ridesharing.

Optimal pricing in ride-share platforms

Optimal pricing in ride-share platforms

Intelligent transport design with a dual focus: Tourist experience and operating cost

Considering further improvements in the intelligent transportation in tourist attraction management is highly appealing. Inspired by the success of the ride-sharing platform, this study designs an intelligent transport system for attractions that can effectively generate user demand-oriented vehicle operation schemes and timely adjust the scheme to match dynamic requests. The system's core components are a vehicle operation scheme optimization module based on an improved heuristic approach and an adaptive updating module that follows time- and quantity-driven rules. Results of a case study in Xiamen indicate that our system significantly outperforms existing systems in improving tourist experience and saving transport operating costs and can provide more diversified and flexible vehicle operation schemes, thereby contributing to economic, social, and environmental benefits.

The complementary effect of ride-sharing on public transit: evidence from a natural experiment

PurposeIdentifying the complementary effects of ride-sharing on public transit is critical to understanding the potential value of growing partnerships between public transit agencies and ride-sharing platforms. The purpose of this paper is to investigate whether and how ride-sharing services complement public transit.Design/methodology/approachTaking advantage of a natural experiment whereby subway Line 2 opened after the entry of ride-sharing services in Xiamen, this study uses a difference-in-differences approach to identify the complementary effects of ride-sharing on public transit based on a proprietary fine-grained trip-level data set from a large ride-sharing platform.FindingsThis study obtained the encouraging finding that ride-sharing has a significant complementary effect on the subway, as the number of ride-sharing pickups and drop-offs at subway stations increased by 130% and 117.9%, respectively, after the subway opening. Moreover, mechanism analysis shows that the complementary effect of ride-sharing services is stronger when connection distance is short (i.e. under 6 km) and when the transportation availability is limited (i.e. at night or in the areas with low transit supply and low population density).Practical implicationsThe findings provide guidelines for promoting cooperation between public transit agencies and ride-sharing platforms to build an efficient and sustainable urban transport system.Originality/valueThis study is the first to examine the complementary effect of ride-sharing services on public transit via unique fine-grained ride-sharing trips data, and further reveal the underlying mechanism behind this effect.

Non-discriminatory operating policies for ridesharing platforms with dual capacities

Many ridesharing platforms operate with dual capacities. They employ full-timers, in addition to independent service providers (also known as self-schedulers). Unlike self-schedulers, full-timers work exclusively for a platform and have limited flexibility. While allocating demand between the two capacities, a platform could prioritize its own full-timers over self-schedulers (preferential policy). This preferential treatment is considered discriminatory and, hence, illegal, even though it may maximize profits. Ridesharing platforms, therefore, have to explore alternative operating policies to overcome this legal hurdle. This study is a step towards helping platform managers identify the best alternative (or at the least the second-best alternative) to the controversial preferential policy. It explores two alternative non-discriminatory policies—contingency capacity policy (self-schedulers get higher priority in demand allocation) and common-pool policy (equal preference to the two capacities). A major finding of this study is that the contingency capacity policy, the most altruistic among the three policies, can deliver optimal profits for asset-light platforms (platforms with fewer full-timers). Managers of asset-light platforms could leverage the altruistic nature of the contingency capacity policy to attract more self-schedulers to join the platform and derive a significant competitive advantage.

Cost Efficiency Evaluation of an On-Chain, Decentralized Ride-Sharing Platform

Traditional taxi providers are slowly losing their clients to online taxi providers, which are becoming increasingly popular. These companies typically provide a commercial, centralized platform in order to provide these services. The concept of centralization gives those providers significant power over their users, such as demanding and changing fees and collecting data over provided services. We aim to discuss how a decentralized approach using blockchains can solve those problems. A ride-sharing platform with the most essential functionality is implemented using a decentralized application based on smart contracts, which are, in turn, deployed on different blockchains. Various effects with respect to implementing and providing a decentralized ride-sharing service are taken in to account, especially regarding the effective cost for users. The fees of the developed platform heavily depend on which blockchain it is deployed on. Blockchains may compete in term of cost with traditional ride-sharing platforms, but choosing a suitable blockchain for the application is essential. Furthermore, the prototype shows that the time it takes for a transaction to be manifested into a block and subsequently confirmed is multiple times longer than a traditional web service response. These new problems require a new approach to implementing those requests that do not negatively affect the user experience.

Dynamic Ridesharing With Minimal Regret: Towards an Enhanced Engagement Among Three Stakeholders

In dynamic ridesharing, the platform serves as the mediator by tailoring the assignment result between workers and riders with a focus on a certain objective. Existing studies generally focus on either one or two stakeholders when modelling the problem while the wellbeing of the other parties may be ignored or even undermined. For example, purely maximizing the total revenue of the ridesharing platform may cause the loss of riders and in turn lead to a low served rate, because those expensive orders will be processed in priority. In this paper, we for the first time study how to incorporate the willingness of all stakeholders (i.e., the platform, workers and riders). Given a set of workers and a set of rider requests, we aim to return the matchable worker-rider pairs in order to minimize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regret</i> . Specifically, two types of regret are defined: (i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">served rate regret</i> , which refers to the rate of unserved requests, catering for the reputation and profit of the platform and workers; (ii) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">revenue regret</i> , which considers the portion of revenue loss from unserved riders, catering for the focus of workers and riders in the trip schedule. We prove the NP-hardness of this problem. To tackle this problem, we first propose a dynamic programming insertion algorithm to improve the efficiency of inserting a rider request into a trip schedule of a worker. Furthermore, two kinds of heuristic algorithms are devised to match rider requests with workers effectively. Comprehensive experiments on two real-world datasets verify the effectiveness, efficiency and scalability of our solutions in dealing with different supply-demand relationships in practice.

Putting Teams into the Gig Economy: A Field Experiment at a Ride-Sharing Platform

The gig economy provides workers with the benefits of autonomy and flexibility but at the expense of work identity and coworker bonds. Among the many reasons why gig workers leave their platforms, one unexplored aspect is the lack of an organization identity. In this study, we develop a team formation and interteam contest field experiment at a ride-sharing platform. We assign drivers to teams either randomly or based on similarity in age, hometown location, or productivity. Having these teams compete for cash prizes, we find that (1) compared with those in the control condition, treated drivers work longer hours and earn 12% higher revenue during the contest; (2) the treatment effect persists two weeks postcontest, albeit with half of the effect size; and (3) drivers in hometown-similar teams are more likely to communicate with each other, whereas those in age-similar teams continue to work longer hours and earn higher revenue during the two weeks after the contest ends. Together, our results show that platform designers can leverage team identity and team contests to increase revenue and worker engagement in a gig economy.This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis.Funding: Financial support from the platform through the Michigan Institute for Data Science is gratefully acknowledged.Supplemental Material: The e-companion are data are available at https://doi.org/10.1287/mnsc.2022.4624 .

Ride-sharing platforms: the effects of online social interactions on loyalty, mediated by perceived benefits

PurposeThis study examines how customer loyalty among DiDi users in China is affected by two types of online social interaction (transactional and interpersonal) and how the online interaction–customer loyalty relationship is mediated by three kinds of perceived benefits (functional, social-hedonic and safety).Design/methodology/approachThis study empirically examines research hypotheses based on a questionnaire survey of 428 DiDi consumers.FindingsThe results reveal that transactional interactions significantly enhance customer loyalty among DiDi users via the partial mediating effects of customers' perceived functional, social-hedonic and safety benefits. By contrast, interpersonal interactions do not directly influence customer loyalty, and only social-hedonic benefits fully mediate the positive influence of interpersonal interactions on loyalty.Originality/valueThis study contributes to the theoretical development of interactive marketing management by examining how two types of online social interactions contribute to customer loyalty on sharing economy platforms by influencing the perception of benefits. It also provides useful managerial insights to help ride-sharing platforms design online social interaction functions that improve customer perceptions and loyalty.

An Empirical Investigation of Ridesharing and New Vehicle Purchase

Problem definition: In this work, we examine how ridesharing platforms affect changes in short-term vehicle purchasing. On the one hand, if the introduction of such platforms motivates owners to use the idle capacity of their existing vehicles to accrue rents, vehicle sales might fall. On the other hand, if the platform induces would-be drivers to purchase new vehicles in order to participate, vehicle sales might rise. Academic/practical relevance: Whereas operations management researchers have begun to broach this subject analytically, this work provides empirical evidence of the impact of ridesharing platforms on new vehicle ownership. Further, we assess heterogeneity in the effect across vehicle type and location. Methodology: We examine this tension using a unique data set of new vehicle registrations in China. In doing so, we exploit the variation in the timing of Uber entry using a difference-in-difference approach. Results: Findings suggest Uber entry is associated with a significant short-term increase in private new vehicle ownership, indicating that consumers actively change their stock of held resources to capture excess rents offered by the platform. These effects exclusively manifest among vehicle brands that qualify for the platform. Further, inasmuch as sales of vehicles with smaller displacement increase more than large-displacement vehicles, results indicate that the effect of Uber entry varies considerably across vehicle types. Finally, results indicate that the effects are stronger in locations where established public transportation options are weaker. Managerial implications: Results provide initial evidence that manufacturers can benefit from the emergence of the sharing economy, especially manufacturers whose products align with the needs of platform participants. For policy makers, our findings further undercut claims made by platforms that the individuals working on them are exploiting already existing resources, suggesting some form of nascent professionalism on the part of platform workers. Funding: The research was supported by the Key Program of National Natural Science of China [Grant 71832010]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1183 .

A Ridesharing Platform with an Oversupply of Drivers: Different Assigning Rates or Commission Rates for Drivers?

A Ridesharing Platform with an Oversupply of Drivers: Different Assigning Rates or Commission Rates for Drivers?

The dimensionality of trust in the relationship between familiarity, reputation, and continuance intention to use an on-demand ridesharing platform

The dimensionality of trust in the relationship between familiarity, reputation, and continuance intention to use an on-demand ridesharing platform

The dimensionality of trust in the relationship between familiarity, reputation, and continuance intention to use an on-demand ridesharing platform

The dimensionality of trust in the relationship between familiarity, reputation, and continuance intention to use an on-demand ridesharing platform

Virtual teams in a gig economy

While the gig economy provides flexible jobs for millions of workers globally, a lack of organization identity and coworker bonds contributes to their low engagement and high attrition rates. To test the impact of virtual teams on worker productivity and retention, we conduct a field experiment with 27,790 drivers on a ride-sharing platform. We organize drivers into teams that are randomly assigned to receiving their team ranking, or individual ranking within their team, or individual performance information (control). We find that treated drivers work longer hours and generate significantly higher revenue. Furthermore, drivers in the team-ranking treatment continue to be more engaged 3 mo after the end of the experiment. A machine-learning analysis of 149 team contests in 86 cities suggests that social comparison, driver experience, and within-team similarity are the key predictors of the virtual team efficacy.

Intention to provide ridesharing services: Determinants from the perspective of driver-partners in a gig economy

Research on ridesharing platforms under the gig economy has focused much on the incentives and barriers of users, leaving many gaps in understanding drivers’ intention to provide ridesharing services. This paper aims to explore, from the perspective of driver-partners, motives that encourage them to continue being gig workers. Data for the study are based on a cross-sectional survey of ridesharing drivers in three metropolitan areas in three regions (North, Central, and South) of Vietnam, conducted from June to July 2022. The paper regresses behavioral intention to continue being a gig driver on their demographic characteristics and self-estimation of economic benefit, time preference, and enjoyment of being a gig driver via ordered probit models. For all three regions, the result suggests that economic benefit, time preference, and enjoyment are good predictors of drivers’ intention to provide the services. Specifically, the probability of remaining in gig work among drivers decreases with their educational and economic status. Higher economic benefit does not predict a higher intention of drivers to stay longer in gig work. Similarly, those with higher levels of enjoyment of traveling and vehicles have a lower intention to remain in this sphere. In the North, the interaction terms between time preference and enjoyment level are significant, suggesting that the effect of enjoyment levels becomes less damaging with an increase in time preference. In other words, time preference is vital in keeping gig drivers in this type of work.

VOMA: A Privacy-Preserving Matching Mechanism Design for Community Ride-Sharing

Providing high-quality matching between drivers and riders is imperative for sustaining the growth of ride-sharing platforms. A user-focused matching mechanism design plays a key role in terms of ensuring user satisfaction. In this paper, we consider the matching problem in the community ride-sharing setting, where drivers and riders have strong personal preferences over the matched counterparties. Obtaining high-quality solutions that accommodate drivers’ and riders’ preferences in such a setting is particularly challenging as drivers and riders maybe reluctant to share with the platform their personal preferences over their ride-sharing counterparties due to privacy and ethical concerns. To this end, we propose a VOting-based MAtching (VOMA) mechanism to compute near-optimal matching solutions for drivers and riders, while preserving their privacy. The mechanism is a distributed implementation of the simulated annealing meta-heuristic, which computes matching solutions by guiding drivers and riders in the distributed search process using an iterative voting protocol. We evaluate the performance of VOMA using test cases generated based on New York taxi data sets. The experiment results show that the proposed matching mechanism achieves on average 90.9% efficiency compared with optimal solutions. We also show that VOMA improves the vehicle miles traveled (VMT) savings by up to 35% compared to an alternative voting-based greedy matching mechanism. System scalability and other practical issues regarding the implementation of such a matching mechanism in community ride-sharing platforms are also discussed.

Technology attachment, e-Attitude, perceived value, and behavioral intentions towards Uber-ridesharing services: the role of hedonic, utilitarian, epistemic, and symbolic value

PurposeThe study aims to empirically test the effects of antecedents on behavioral intentions towards Uber-ridesharing services. The antecedents are perceived value (hedonic, utilitarian, epistemic, and symbolic value), e-Attitude, and technology attachment (smartphone use, Internet use, and e-Involvement). Moreover, the study explores the mediating effect of three-dimensional perceived value (hedonic, utilitarian, and epistemic value) and e-Attitude; and the moderating effect of symbolic value on behavioral intentions towards Uber-ridesharing services.Design/methodology/approachA mixed survey (75% Google Form, 25% face to face) was conducted in Bangladesh to collect data from customers who had previously participated in Uber-ridesharing services, one of the largest ridesharing platforms in Bangladesh. Subsequently, data were analyzed based on the structural equation modeling technique using SmartPLS 3.3.3.FindingsThe study findings revealed that hedonic value, utilitarian value, epistemic value, symbolic value, e-Attitude, smartphone use, internet use, e-Involvement had a direct significant positive impact on behavioral intentions. Also, e-Attitude significantly impacted hedonic, utilitarian, and epistemic value. In addition, Smartphone use, internet use, and e-Involvement significantly influenced e-Attitude. Moreover, the study findings revealed that hedonic, utilitarian, and epistemic value partially mediates between e-Attitude and behavioral intentions; and e-Attitude partially mediates between Smartphone use, Internet use, and e-Involvement and hedonic, utilitarian, and epistemic value and behavioral intentions. Furthermore, the results indicate that epistemic value significantly moderates the relationship between hedonic, utilitarian, and epistemic value and behavioral intentions.Practical implicationsThis study uncovers some insightful findings for ridesharing services providers and managers helping to build customers' positive behavioral intentions towards Uber-ridesharing services. In particular, practitioners can improve cost-efficiency, hedonic and symbolic aspects, availability of rides of Uber-ridesharing services. Moreover, the ridesharing services managers should adopt technology-based service opportunities.Originality/valueThe study enriches sharing economy literature, especially ridesharing services, exploring the direct effect of epistemic value, e-Attitude, smartphone use, Internet use, and e-Involvement on behavioral intentions. Moreover, this study presents smartphone use, Internet use, and e-Involvement as new antecedents of e-Attitude and behavioral intentions. Furthermore, the study explores the mediating effect of hedonic, utilitarian, and epistemic value and e-Attitude; and the moderating effect of symbolic value in Uber-ridesharing service perspective.

Intercity ridesharing to the rescue: Capacity flexibility and price stability of BlaBlaCar during the 2018 French railway strike

The increased usage of alternative transportation modes during public transportation disruptions has been well documented in the literature. While many papers discuss the resilience of the disrupted mode or measure changes in the usage of various alternative modes, little research has addressed the capacity flexibility and the price fluctuations of the alternative modes—two important components of the quality of an alternative. This paper documents the capacity and price changes of BlaBlaCar during the nationwide French railway strike in 2018. We collected more than 1 million trip offers from the BlaBlaCar’s API from April to July 2018, covering 82 representative intercity routes in France. Our empirical analysis shows that, on an average strike day, the number of offered seats increased by approximately 6 %, while the number of booked seats rose by 33 %. Despite the spikes in demand, prices remained stable during the strike. We argue that the price recommendation mechanism helped maintain the price stability on the platform during the strike. The mechanism is in fact an effective enforcement of the policy laid down by the French government to uphold the cost-sharing principle on ridesharing platforms. This paper presents a case of voluntary compliance by a private company to put in place a price recommendation mechanism that in effect stabilizes prices and contributes to the resilience of the whole transportation system against abnormal market conditions.

Dynamic Order Dispatching With Multiobjective Reward Learning

Traffic supply-demand mismatching has a severe impact on intelligent transportation systems. Fortunately, order dispatching is a promising option to mitigate the traffic supply-demand imbalance. Along this line, this article proposes the Multi-Driver Multi-Order Dispatching (MDMOD) method to make efficient order dispatching policy and enhance the experience of drivers and passengers. In the proposed MDMOD method, the Dynamic Multi-Objective Reward Learning (DMRL) algorithm is proposed to measure the driver-order-pair value, which illustrates the importance of a driver serving a specific order. A centralized matching algorithm is introduced to match all drivers and orders to maximize all driver-order-pair values. The multi-objective reward in the DMRL algorithm considers both immediate gains (i.e., pick-up distance) and future gains (i.e., the future traffic demand of order destination) to effectively improve the experience of drivers and passengers. Furthermore, by introducing the driver service level into the multi-objective reward, the “outstanding driver better reward” mechanism is realized to promote the ecological development of ride-sharing platforms. Notably, the Temporal-Graph Convolutional Network algorithm is proposed to predict the future traffic demand. Some virtual orders, which generated with the predicted future traffic demand, are dispatched to idle drivers to multiplex the traffic supply fully. A simulator is designed to test the performance of the proposed MDMOD method, experimental results demonstrate that the MDMOD method outperforms the state-of-the-art methods in terms of Average Driver Income and Order Response Rate.