Bike Sharing

Freund, Daniel; Henderson, Shane G.; Shmoys, David B.

doi:10.1007/978-3-030-01863-4_18

Cited by 29 publications

(13 citation statements)

References 37 publications

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“…Although the literature on the design, planning, and operation of on-demand vehicle sharing systems is extensive and growing (see for example recent reviews in Freund et al (2019), He et al (2019), and Benjaafar and Hu (2020)), papers that consider optimal fleet sizing, while accounting for the underlying queueing dynamics, are relatively few. One of the earliest papers to consider a problem similar to ours is George and Xia (2011).…”

Section: Related Literaturementioning

confidence: 99%

Dimensioning On-Demand Vehicle Sharing Systems

Benjaafar

Liu

et al. 2022

Management Science

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We consider the problem of optimal fleet sizing in a vehicle sharing system. Vehicles are available for short-term rental and are accessible from multiple locations. A vehicle rented at one location can be returned to any other location. The size of the fleet must account not only for the nominal load and for the randomness in demand and rental duration but also for the randomness in the number of vehicles that are available at each location because of vehicle roaming (vehicles not returning to the same location from which they were picked up). We model the dynamics of the system using a closed queueing network and obtain explicit and closed form lower and upper bounds on the optimal number of vehicles (the minimum number of vehicles needed to meet a target service level). Specifically, we show that starting with any pair of lower and upper bounds, we can always obtain another pair of lower and upper bounds with gaps between the lower and upper bounds that are independent of demand and bounded by a function that depends only on the prescribed service level. We show that the generated bounds are asymptotically exact under several regimes. We use features of the bounds to construct a simple and closed form approximation that we show to be always within the generated lower and upper bounds and is exact under the asymptotic regimes considered. Extensive numerical experiments show that the approximate and exact values are nearly indistinguishable for a wide range of parameter values. The approximation is highly interpretable with buffer capacity expressed in terms of three explicit terms that can be interpreted as follows: (1) standard buffer capacity that is protection against randomness in demand and rental times, (2) buffer capacity that is protection against vehicle roaming, and (3) a correction term. Our analysis reveals important differences between the optimal sizing of standard queueing systems (where servers always return to the same queue upon service completion) and that of systems where servers, upon service completion, randomly join any one of the queues in the system. We show that the additional capacity needed to buffer against vehicle roaming can be substantial even in systems with vanishingly small demand. This paper was accepted by Baris Ata, stochastic models and simulation.

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Section: Related Literaturementioning

confidence: 99%

Dimensioning On-Demand Vehicle Sharing Systems

Benjaafar

Liu

et al. 2022

Management Science

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“…As a further example, [27] substitute the censored observations with the mean of the historical (non-censored) observations regarding the same period. A different but related approach is proposed by [29,30] who focus on obtaining an unbiased estimate of arrival rate by omitting historical instances where bikes were unavailable.…”

Section: Common Approaches To Handling Demand Censorshipmentioning

confidence: 99%

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes

Gammelli¹,

Peled²,

Rodrigues³

et al. 2020

Preprint

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Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we devise a censored likelihood function. We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.

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“…Demaio [12] and Shaheen et al [13] discuss the history and future directions of BSS in detail. Readers are referred to [14][15][16] for more recent reviews. Gavalas et al [17] examine existing literature concerning the design and management of vehicle-sharing systems, and classify them into strategic, tactical, and operational levels, based on the planning horizon.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information

Liu²,

Shi

2019

Sustainability

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Free-float bike-sharing (FFBS) systems have increased in popularity as a sustainable travel mode in recent years, especially in the urban areas of China. Despite the convenience such systems offer to customers, it is not easy to maintain an effective balance in the distribution of bikes. This study considers the dynamic rebalancing problem for FFBS systems, whereby user-based tactics are employed by incentivizing users to perform repositioning activities. Motivated by the fact that the problem is frequently faced by FFBS system operators entering a new market with limited information on travel demand, we adopt the ranking and selection approach to select the optimal incentive plan. We describe the system dynamics in detail, and formulate a profit maximization problem with a constraint on customer service level. Through numerical studies, we first establish that our procedure can select the optimal incentive plan in a wide range of scenarios. Second, under our incentive plan, the profit and service level can be improved significantly compared with the scenario without incentive provision. Third, in most cases, our procedure can achieve the optimal solution with a reasonable sample size.

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Bike Sharing

Cited by 29 publications

References 37 publications

Dimensioning On-Demand Vehicle Sharing Systems

Dimensioning On-Demand Vehicle Sharing Systems

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes

Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information

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