We are on the Way: Analysis of On-Demand Ride-Hailing Systems

Feng, Guiyun; Kong, Guangwen; Wang, Zizhuo

doi:10.2139/ssrn.2960991

Cited by 32 publications

(16 citation statements)

References 22 publications

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“…A disadvantage of such matching is that it could result in long pickup times under certain dispatching policies. Feng et al (2018) show that this is the case when the system utilization is in the midrange and the service area is large. The effect of pickup times on the efficiency of ride-hailing services is also studied in Castillo et al (2018), who use the phrase "wild goose chase" to refer to the phenomenon of dispatching Benjaafar and Hu: Operations Management in the Age of the Sharing Economy drivers to far-away ride requests.…”

Section: On-demand Service Platformsmentioning

confidence: 87%

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Benjaafar

2020

M&SOM

213

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Please scroll down for article-it is on subsequent pagesWith 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit Abstract. The sharing economy, a term we use to refer to business models built around ondemand access to products and services mediated by online platforms that match many small suppliers or service providers to many small buyers, has emerged as an important area of study in operations management. We first describe three "canonical" applications that have garnered much attention from the operations management community. We use these applications to highlight distinguishing features of sharing economy business models and to point out research questions that are new. Then we draw connections between classical operations management theory and models and those that have been used to study sharing economy applications. We do so to put in context some of the recent work on the sharing economy and to showcase the underlying modeling toolkit and identify opportunities for future research.

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Section: On-demand Service Platformsmentioning

confidence: 87%

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Benjaafar

2020

M&SOM

213

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“…Feng et al (2017) compared the average rider waiting time for street‐hailing (where the rider hails the first passing driver) to that for ride‐hailing with the first‐dispatch protocol, in a simulation. The authors found that ride‐hailing generally has lower rider waiting time.…”

Section: Matching In Ride‐hailingmentioning

confidence: 99%

“…The story is similar on the rider side. Feng et al (2017) demonstrated via simulations that the average rider waiting time between request and pickup ( rider waiting time ) can be much lower in a ride‐hailing system compared with that in a street‐hailing taxi system (Figure 1). Rider waiting time and capacity utilization rate are also closely related to a concept of reliability of the service: sudden spikes in demand (for instance, at the end of a concert) can cause a dramatic increase in the rider waiting time and drop in the capacity utilization rate, leading to a poor experience on both sides.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic pricing and matching in ride‐hailing platforms

Yan

Zhu

Korolko

et al. 2019

Naval Research Logistics

213

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Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.

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“…look at different pricing schemes and show the advantages of the widely used surge pricing policy Hu and Zhou (2018). analyze the performance of the fixed commission contract which is used by many on-demand sharing platforms in practice Feng et al (2018). compare the new on-demand ride-hailing system with the traditional street-hailing system, and propose methods to improve the performance of the on-demand hailing system.Our model and results on the low-cost airline baggage case add to the study of commercial airlines' baggage policy.…”

mentioning

confidence: 99%

From Low-Cost Airline Baggage to By-Minute Car Rental, the Impact of Penalty Cost on Customers' Booking Decisions

Long,

Wang,

Xie

2018

SSRN Journal

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This paper studies two problems that are both related to the impact of shortage penalty cost on customers' booking decisions. The first problem looks at low-cost airline passengers' baggage allowance purchasing decisions. The second problem looks at by-minute car rental customers' rental booking decisions. Both of the problems are newsvendor type of problems. However, from practical observations, customers' baggage booking allowance (rental booking time duration) does not monotonically increase with the baggage overweight (return delay) penalty cost (i.e., the shortage cost), which is unexpected for newsvendor systems. In this paper, we explain this interesting phenomenon by looking into the influence of shortage penalty cost on customers' baggage (trip) planning and weight (time) estimation, which in turn, affects the actual baggage weight (usage time) distribution. We then incorporate this into stochastic decision models and analytically characterize how important quantities (e.g., customers' booking allowance, customers' total cost, and firm's profit) change with the penalty cost. We prove that both the customers' booking allowance and the firm's profit are quasiconcave in the penalty cost. For the by-minute car rental problem, we also study the optimal delay penalty cost for the rental firm to maximize its profit. We show that, when a rental car is shared by more customers, a higher delay penalty cost is preferred.

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We are on the Way: Analysis of On-Demand Ride-Hailing Systems

Cited by 32 publications

References 22 publications

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Dynamic pricing and matching in ride‐hailing platforms

From Low-Cost Airline Baggage to By-Minute Car Rental, the Impact of Penalty Cost on Customers' Booking Decisions

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