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

Abstract

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/.

Deployment Optimization for Shared e-Mobility Systems With Multi-Agent Deep Neural Search

“…Shared Mobility Services. Recently there is a surging research interests in shared mobility systems in cities, e.g., from understanding taxi demand [3] to reposition shared bikes [4] and optimising shared mobility systems to achieving the desired balance between profit and fairness [5]. EVsharing in particular, as one of the environmental friendly services, has attracted extensive attention from various angles and incubated many interesting problems, such as energy consumption estimation [6], charging scheduling [7], [8] and infrastructure planning [9].…”

Fairness Maximization among Offline Agents in Online-Matching Markets

“…There are several other works that considered fairness in matching in an offline setting where all agents' information is given as part of the input, see, e.g., [34,35]. Nanda et al [36] proposed a bi-objective online-matching-based model to study the tradeoff between the system efficiency (profit) and the fairness among rideshare riders during high-demand hours. In contrast, Xu and Xu [37] presented a similar model to examine the tradeoff between the system efficiency and the income equality among rideshare drivers.…”

Equity Promotion in Online Resource Allocation

“…Justification of parameters {𝜇 𝑔 }. Observe that previous studies (Ma, Xu, and Xu 2020;Nanda et al 2020;Xu and Xu 2020) have chosen a fairness metric as min 𝑔 ∈ G E[𝑋 𝑔 ]/E[ 𝐴 𝑔 ], the minimum ratio of the expected number of agents served to that of arrivals among all groups. This can be cast as a special case of ASR by setting each…”

“…One technical challenge in addressing (internal) equity promotion in online resource allocation is how to craft a proper metric of equity. Current metrics of equity proposed so far are all based on the minimum ratio of the number of requesters served to that of total arrivals within any given group (Ma, Xu, and Xu 2020;Nanda et al 2020;Xu and Xu 2020). Unfortunately, these metrics fail to capture the exact equity we expect in practice.…”