Spatial Pricing in Ride-Sharing Networks

Bimpikis, Kostas; Candogan, Ozan; Sabán, Daniela

doi:10.2139/ssrn.2868080

Cited by 89 publications

(127 citation statements)

References 18 publications

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“…(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., 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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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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“…Algorithm 1: NDL-based user optimal routing algorithm 1 Routing D F rs (t), F k rs (t), α, lag ; Input : NDL D F rs , path flow F k rs (t), control ratio α, delay of NDL information lag Output: Adjusted path flowF k rs (t) 2 InitializeF k rs (t) = F k rs (t), ∀rs ∈ K q , k ∈ K rs ; 3 Initialize list L as an empty list; 4 Compute controllable total flow R = α rs k F k rs (t); 5 Sort OD pairs by D F rs (t − lag) in descending order and store the list in L; 6 while R > 0 and |L| > 0 do 7 Pop the OD pair rs with highest D F rs (t − lag) in L; 8 Search for the shortest path k based on v i , i ∈ N rs (t − lag) for trajectory-level information or C rτ s (t − lag) for zone-to-zone travel time; 9 Route F k rs (t), k = k to path k,F k rs (t) =F k rs (t) + k =k F k rs (t),F k rs (t) = 0;…”

Section: Traffic Management Through User Optimal Routingmentioning

confidence: 99%

“…TNCs match the riders and drivers in real-time and instruct drivers to pick-up and drop-off riders through a real-time routing mechanism. According to a recent report [50], Uber alone has covered 551 cities globally and surpassed two billion rides by July 2016.The proliferation of ride-sourcing services has profoundly reshaped of transportation systems and hence stimulated broad discussions and research from various perspectives, including labor market of ride-sourcing drivers and rider behaviors [18,46,25], stochastic vehicle dispatching [35], pricing strategies [8,4], optimal parking provision [57] as well as policies and regulations [45,61,60]. RV-related data sets include, but are not limited to:i) Survey data.…”

mentioning

confidence: 99%

Measuring and reducing the disequilibrium levels of dynamic networks with ride-sourcing vehicle data

Qian

2020

Transportation Research Part C: Emerging Technologies

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“…, m} equidistant nodes that can each serve as a trip origin or destination 1 . We adopt the static model studied in [21] for the customers' transportation demand. We assume that potential customers arrive at node i at a rate of θ i per period.…”

Section: Network and Demand Modelsmentioning

confidence: 99%

Smart Charging Benefits in Autonomous Mobility on Demand Systems

Turan

Tucker

Alizadeh

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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In this paper, we study the potential benefits from smart charging for a fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD) services. We first consider a profit-maximizing platform operator who makes decisions for routing, charging, rebalancing, and pricing for rides based on a network flow model. Clearly, each of these decisions directly influence the fleet's smart charging potential; however, it is not possible to directly characterize the effects of various system parameters on smart charging under a classical network flow model. As such, we propose a modeling variation that allows us to decouple the charging and routing problems faced by the operator. This variation allows us to provide closedform mathematical expressions relating the charging costs to the maximum battery capacity of the vehicles as well as the fleet operational costs. We show that investing in larger battery capacities and operating more vehicles for rebalancing reduces the charging costs, while increasing the fleet operational costs. Hence, we study the trade-off the operator faces, analyze the minimum cost fleet charging strategy, and provide numerical results illustrating the smart charging benefits to the operator.

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Spatial Pricing in Ride-Sharing Networks

Cited by 89 publications

References 18 publications

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

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

Measuring and reducing the disequilibrium levels of dynamic networks with ride-sourcing vehicle data

Smart Charging Benefits in Autonomous Mobility on Demand Systems

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