Online minimum matching in real-time spatial data

Tong, Yongxin; She, Jieying; Ding, Bolin; Chen, Lei; Wo, Tianyu; Xu, Ke

doi:10.14778/2994509.2994523

Cited by 179 publications

(77 citation statements)

References 27 publications

(38 reference statements)

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“…They assumed that each driver can only share the trip with one other rider, which limited the potential of ridesharing system. Other recent studies on task assignment [38], [39] exploit bilateral matching between a set of workers and tasks to achieve one single objective, minimizing the total distance [38] or maximizing the total utility [39]. However, when the ordering of multiple riders must also be mapped to a single driver, bilateral mapping is not sufficient.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Ridesharing in Peak Travel Periods

Luo

Bao

Choudhury

et al. 2021

IEEE Trans. Knowl. Data Eng.

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In this paper, we propose and study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and a set of rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing ridesharing solutions in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing ridesharing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost (either distance or time). (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per newly coming rider requests should be incorporated, while it is unfortunately excluded by most existing solutions. In order to achieve our goal, we propose an underlying index structure on top of a partitioned road network, and compute the lower bounds of the shortest path distance between any two vertices. Using the proposed index together with a set of new pruning rules, we develop an efficient algorithm to dynamically include new riders directly into an existing trip schedule of a driver. In order to respond to new rider requests more effectively, we propose two algorithms that bilaterally match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the effectiveness and efficiency of the proposed methods.Index Terms-Dynamic ridesharing, peak hour, index structure, pruning rules ! • H. Luo, Z. Bao (corresponding author) and J. Culpepper are with the

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Section: Related Workmentioning

confidence: 99%

Dynamic Ridesharing in Peak Travel Periods

Luo

Bao

Choudhury

et al. 2021

IEEE Trans. Knowl. Data Eng.

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“…Our problem is a form of online matching in dynamic environments, which is an active area of research within the AI/ML community. In particular, [10,21,33,31] have studied algorithms for matching in various dynamic markets such as kidney exchange, spatial crowdsourcing, labor markets, and so on. Online matching in static environments has been extensively studied in the literature; see Mehta et al [23] for a detailed survey.…”

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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“…The problem studied in this paper is an extension of the task assignment problem in spatial crowdsourcing, known as the server-assigned task assignment problem [10,11], in which workers cannot reject the assigned tasks. Recently, task assignment in real-time spatial crowdsourcing has also been studied by the online algorithmic model [12,23]. Based on the original task assignment problem, both [24,25] study the conflict-aware task assignment problem, in which tasks may conflict with each other and thus cannot be assigned to the same worker.…”

Section: Spatialmentioning

confidence: 99%

Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data

Qian

Zhu

et al. 2017

Journal of Sensors

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These days, with the increasingly widespread employment of sensors, particularly those attached to vehicles, the collection of spatial data is becoming easier and more accurate. As a result, many relevant areas, such as spatial crowdsourcing, are gaining ever more attention. A typical spatial crowdsourcing scenario involves an employer publishing a task and some workers helping to accomplish it. However, most of previous studies have only considered the spatial information of workers and tasks, while ignoring individual variations among workers. In this paper, we consider the Software Development Team Formation (SDTF) problem, which aims to assemble a team of workers whose abilities satisfy the requirements of the task. After showing that the problem is NP-hard, we propose three greedy algorithms and a multiple-phase algorithm to approximately solve the problem. Extensive experiments are conducted on synthetic and real datasets, and the results verify the effectiveness and efficiency of our algorithms.

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Online minimum matching in real-time spatial data

Cited by 179 publications

References 27 publications

Dynamic Ridesharing in Peak Travel Periods

Dynamic Ridesharing in Peak Travel Periods

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

Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data

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