An Optimization Framework for Online Ride-Sharing Markets

Jia, Yongzheng; Xu, Wei; Xue, Lei

doi:10.1109/icdcs.2017.185

Cited by 31 publications

(23 citation statements)

References 19 publications

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“…More importantly, the application allows drivers to either drive for free, or to be algorithmically compensated based on miles/kilometers driven by GPS and the government reimbursement rate [90]. The Waze Carpool application also limits the number of rides an individual can offer or receive in one day [91]. Another example is BlaBlaCar, a form of money-based carpooling which functions as a marketplace where drivers post their routes with prices, and passengers may buy seats [90].…”

Section: Carpoolingmentioning

confidence: 99%

An Overview of Shared Mobility

et al. 2018

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In a wider understanding, shared mobility can be defined as trip alternatives that aim to maximize the utilization of the mobility resources that a society can pragmatically afford, disconnecting their usage from ownership. Then, shared mobility is the short-term access to shared vehicles according to the user’s needs and convenience. The contributions and added value of this paper are to provide an up-to-date and well-structured review on the area of shared mobility to researchers and practitioners of the transport sector. Hence, this paper presents a bibliographical review of shared mobility and its diverse modalities, as an alternative to individual transportation, especially in cases of individual automobiles or short trips restricted to an urban city. The present literature review on shared modes of transportation has discovered that the introduction of these modes alone will not solve transportation problems in large cities, with elevated and growing motorization rates. However, it can among the strategies employed to help alleviate the problems caused by traffic jams and pollution by reducing the number of vehicles in circulation, congestions, and the urban emission of polluting gases. Thus, the implementation of shared mobility schemes offers the potential to enhance the efficiency, competitiveness, social equity, and quality of life in cities. This paper covers the fundamental aspects of vehicle and/or ride sharing in urban centers, and provides an overview of current shared mobility systems.

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Section: Carpoolingmentioning

confidence: 99%

An Overview of Shared Mobility

et al. 2018

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“…We then conclude that y ′ i , x i satisfies (20). Overall, the solution is feasible and hence provides a lower bound for the optimal value of the problem MP-AN, denoted as OPT , i.e.,…”

Section: Prove Of Theoremmentioning

confidence: 75%

“…The authors in [9] propose a 2.5approximation algorithm under a constrained setting. Many studies consider efficient heuristics and metaheuristics [6,10,16,20,24,30,32]. These offline solutions may serve as performance benchmarks, but they are usually not practical.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization

Lin

Chen

et al. 2019

Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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Ride-sharing is a modern urban-mobility paradigm with tremendous potential in reducing congestion and pollution. Demand-aware design is a promising avenue for addressing a critical challenge in ride-sharing systems, namely joint optimization of request-vehicle assignment and routing for a fleet of vehicles. In this paper, we develop a probabilistic demand-aware framework to tackle the challenge. We focus on maximizing the expected number of passenger pickups, given the probability distributions of future demands. The key idea of our approach is to assign requests to vehicles in a probabilistic manner. It differentiates our work from existing ones and allows us to explore a richer design space to tackle the requestvehicle assignment puzzle with a performance guarantee but still keeping the final solution practically implementable. The optimization problem is non-convex, combinatorial, and NP-hard in nature. As a key contribution, we explore the problem structure and propose an elegant approximation of the objective function to develop a dual-subgradient heuristic. We characterize a condition under which the heuristic generates a (1 − 1/e) approximation solution. Our solution is simple and scalable, amendable for practical implementation. Results of numerical experiments based on real-world traces in Manhattan show that, as compared to a conventional demand-oblivious scheme, our demand-aware solution improves the passenger pickups by up to 46%. The results also show that joint optimization at the fleet level leads to 19% more pickups than that by separate optimizations at individual vehicles.

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“…Stateindependent policies were studied previously using theory from control and queuing systems [28,6,4]. Apart from using Markov-chain methods, many allocation and scheduling problems have been studied in the rideshare context using methods from combinatorial optimization and machine learning (e.g., [11,9,16]). A large-scale mathematical and empirical study on the number of cars and the optimality of waiting times was recently analyzed by Alonso-Mora et al [1].…”

Section: Assumptions and Related Workmentioning

confidence: 99%

Mix and Match: Markov Chains and Mixing Times for Matching in Rideshare

Curry

Dickerson

Sankararaman

et al. 2019

Web and Internet Economics

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Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests. We model the dispatching process in rideshare as a Markov chain that takes into account the geographic mobility of both drivers and riders over time. Prior work explores dispatch policies in the limit of such Markov chains; we characterize when this limit assumption is valid, under a variety of natural dispatch policies. We give explicit bounds on convergence in general, and exact (including constants) convergence rates for special cases. Then, on simulated and real transit data, we show that our bounds characterize convergence rates-even when the necessary theoretical assumptions are relaxed. Additionally these policies compare well against a standard reinforcement learning algorithm which optimizes for profit without any convergence properties. IntroductionRideshare firms such as Uber, Lyft, and Didi Chuxing dynamically match riders to drivers via an online, digital platform. Riders request a driver through an online portal or mobile app; a driver is matched by the platform to a rider based on geographic proximity, driver preferences, pricing, and other factors. The rideshare driver then picks up the rider at her request location, transfers her to her destination, and reenters the platform to be matched again-albeit at a new geographic location. Part of the larger sharing economy, rideshare firms are increasingly competitive against traditional taxi services due to their ease of use, lower pricing, and immediacy of service [14].Matching riders to rideshare drivers is nontrivial. While the core process is a form of the well-studied online matching problem [23], current models developed in the EconCS, AI, and Operations Research communities [28,11,5] do not completely capture the mobile aspects of both the drivers and riders. Drivers and riders are agents who move about a constrained space (e.g., city streets), becoming (in)active periodically due to the matching process. When a platform receives a request, it must make a near-real-time dispatch decision amongst nearby drivers who are available at the current time. The platform's goal is to maximize an objective (e.g., revenue or throughput) by servicing requests in an online fashion, subject to various realworld constraints and challenges like setting prices, predicting supply and demand, fairness considerations, competing with other firms, and so on [8,17,4].In this paper, we study the dynamics of the nascent rideshare market under different dispatch strategies. Recent work uses Markov chains to model complex ride-sharing dynamics in a closed-world system-that is, a system with a fixed total supply of cars [28,4,5]. These assume the Markov chains reach their stationary distributions quickly, and thus all prior work optimizes for dispatch strategies in the limit. As a complement, the present paper characterizes-theoretically and empirically-when that limit assumption is valid under a variety of natural strategies.

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An Optimization Framework for Online Ride-Sharing Markets

Cited by 31 publications

References 19 publications

An Overview of Shared Mobility

An Overview of Shared Mobility

A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization

Mix and Match: Markov Chains and Mixing Times for Matching in Rideshare

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