Pricing and Optimization in Shared Vehicle Systems: An Approximation Framework

Banerjee, Siddhartha; Freund, Daniel; Lykouris, Thodoris

doi:10.1287/opre.2021.2165

Cited by 53 publications

(47 citation statements)

References 27 publications

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“…In Banerjee et al (2016), the focus is on pricing as a control mechanism in the infinite supply regime. Motivated by the asymptotic regime and control mechanism considered in this paper, the new version Banerjee et al (2017) proves that in our Theorem 2, the ratio between the optimal value of the optimization problem for the finite-sized system and the optimal value of the fluid-based optimization goes to one at a rate of 1 − 1/ √ N ; see appendix D.1 there.…”

Section: Related Literaturementioning

confidence: 85%

Empty-Car Routing in Ridesharing Systems

Braverman¹,

Dai

Liu

et al. 2019

Operations Research

190

112

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This paper considers a closed queueing network model of ridesharing systems such as Didi Chuxing, Lyft, and Uber. We focus on empty-car routing, a mechanism by which we control car flow in the network to optimize system-wide utility functions, e.g. the availability of empty cars when a passenger arrives. We establish both process-level and steady-state convergence of the queueing network to a fluid limit in a large market regime where demand for rides and supply of cars tend to infinity, and use this limit to study a fluid-based optimization problem. We prove that the optimal network utility obtained from the fluid-based optimization is an upper bound on the utility in the finite car system for any routing policy, both static and dynamic, under which the closed queueing network has a stationary distribution. This upper bound is achieved asymptotically under the fluid-based optimal routing policy. Simulation results with real-world data released by Didi Chuxing demonstrate the benefit of using the fluid-based optimal routing policy compared to various other policies.

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Section: Related Literaturementioning

confidence: 85%

Empty-Car Routing in Ridesharing Systems

Braverman¹,

Dai

Liu

et al. 2019

Operations Research

190

112

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“…Our work lies in the general theme of providing supply-dependent guarantees for pricing with known priors and limited supply. Beyond prophet inequalities, such guarantees have also been provided in ridesharing settings [BFL17,BBC19]. The latter works typically make a stronger assumption that the system is in steady state but has more complex state externalities: in multi-unit prophet inequalities, the supply just decreases when items are sold; in ridesharing it is reallocated across the network.…”

Section: Related Workmentioning

confidence: 99%

Static pricing for multi-unit prophet inequalities

Chawla¹,

Devanur²,

Lykouris³

2020

Preprint

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We study a pricing problem where a seller has k identical copies of a product, buyers arrive sequentially, and the seller prices the items aiming to maximize social welfare. When k = 1, this is the so called prophet inequality problem for which there is a simple pricing scheme achieving a competitive ratio of 1/2. On the other end of the spectrum, as k goes to infinity, the asymptotic performance of both static and adaptive pricing is well understood.We provide a static pricing scheme for the small-supply regime: where k is small but larger than 1. Prior to our work, the best competitive ratio known for this setting was the 1/2 that follows from the single-unit prophet inequality. Our pricing scheme is easy to describe as well as practical -it is anonymous, non-adaptive, and order-oblivious. We pick a single price that equalizes the expected fraction of items sold and the probability that the supply does not sell out before all customers are served; this price is then offered to each customer while supply lasts. This pricing scheme achieves a competitive ratio that increases gradually with the supply and approaches to 1 at the optimal rate. Astonishingly, for k < 20, it even outperforms the state-of-the-art adaptive pricing for the small-k regime [Ala11].

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“…The first studies matching policy design in the rideshare setting (i.e., matching riders and drivers), e.g., (Zhao et al 2019;Ashlagi et al 2019;Lowalekar, Varakantham, and Jaillet 2018;Tong et al 2016b;Tong et al 2016a;Tong et al 2017;Bei and Zhang 2018;Dickerson et al 2018b;Dickerson et al 2018a). The second considers the spatial-temporal pricing aspects of rideshare, e.g., (Ma, Fang, and Parkes 2019;Bimpikis, Candogan, and Saban 2017;Kanoria and Qian 2019;Banerjee, Freund, and Lykouris 2017;Banerjee, Johari, and Riquelme 2016). The third focuses on applying reinforcement-learning approaches to planning and matching problems in rideshare see, e.g., (Xu et al 2018;Lin et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours

Nanda

Sankararaman

et al. 2020

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that drivers may have over certain types of requests. We consider the case of peak hours when the demand for rides is more than the supply of drivers. Drivers are well aware of their advantage during the peak hours and can choose to be selective about which rides to accept. Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e.g., from a nonpopular pickup location, or to a non-popular drop-off location) might never be assigned to a driver. Such a system can be highly unfair to riders. However, increasing fairness might come at a cost of the overall profit made by the rideshare platform. To balance these conflicting goals, we present a flexible, non-adaptive algorithm, NAdap, that allows the platform designer to control the profit and fairness of the system via parameters α and β respectively. We model the matching problem as an online bipartite matching where the set of drivers is offline and requests arrive online. Upon the arrival of a request, we use NAdap to assign it to a driver (the driver might then choose to accept or reject it) or reject the request. We formalize the measures of profit and fairness in our setting and show that by using NAdap, the competitive ratios for profit and fairness measures would be no worse than α/e and β/e respectively. Extensive experimental results on both real-world and synthetic datasets confirm the validity of our theoretical lower bounds. Additionally, they show that NAdap under some choice of (α, β) can beat two natural heuristics, Greedy and Uniform, on both fairness and profit. Code is available at: https://github.com/ nvedant07/rideshare-fairness-peak/.

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Pricing and Optimization in Shared Vehicle Systems: An Approximation Framework

Cited by 53 publications

References 27 publications

Empty-Car Routing in Ridesharing Systems

Empty-Car Routing in Ridesharing Systems

Static pricing for multi-unit prophet inequalities

Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours

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