Beyond Rebalancing: Crowd-Sourcing and Geo-Fencing for Shared-Mobility Systems

He, Qiao‐Chu; Nie, Tiantian; Yang, Young-Joon; Shen, Zuo‐Jun Max

doi:10.2139/ssrn.3293022

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…Together with (15), we have the equality (13). Furthermore, given the optimal rateλ * p for FCP with M, we have confirmed in (14) that κλ * p is also an admissible control to FCP with M κ , where the state process is given by κq * .…”

Section: Proof Following Theorem 31 There Existsλsupporting

confidence: 66%

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Optimal Control of a Time-Varying Double-Ended Production Queueing Model

Lee

Liu

et al. 2019

SSRN Journal

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Section: Proof Following Theorem 31 There Existsλsupporting

confidence: 66%

“…Double-ended queues have been studied for many applications, including taxi-service systems, perishable inventory systems, organ transplant systems, and finance (cf. [14,1,36,32,7,13]). In particular, [14] studied a perishable inventory system with Poisson arrivals for both supplies and demands.…”

mentioning

confidence: 99%

Optimal Control of a Time-Varying Double-Ended Production Queueing Model

Lee

Liu

et al. 2019

SSRN Journal

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“…To estimate Z , an intuitive approach would be to predict the total bike departures N μ and bike arrivals N λ , and then calculate Z = N μ − N λ . Bike departures and arrivals have been widely modeled as Poisson flows [ 19 , 28 – 33 ], so a Poisson regression is an intuitive candidate model. A Poisson regression essentially models the expected value of the dependent variable through a linear combination of a set of independent variables X as: …”

Section: Methodsmentioning

confidence: 99%

“…While several distributions have been used to model the bike arrivals (and departures) within a bike sharing, including negative binomial [ 24 ], Weibull [ 25 – 27 ] and Poisson [ 28 – 33 ], the latter is the most common choice for this task. Gast et al .…”

Section: Introductionmentioning

confidence: 99%

Excess demand prediction for bike sharing systems

Liu

Pelechrinis

2021

PLoS ONE

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One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing. While different operators have different approaches to this functionality, overall it requires a demand-supply analysis of the various stations. While trip data can be used for this task, the existing methods in the literature only capture the observed demand and supply rates. However, the excess demand rates (e.g., how many customers attempted to rent a bike from an empty station) are not recorded in these data, but they are important for the in-depth understanding of the systems’ demand patterns that ultimately can inform operations like rebalancing. In this work we propose a method to estimate the excess demand and supply rates from trip and station availability data. Key to our approach is identifying what we term as excess demand pulse (EDP) in availability data as a signal for the existence of excess demand. We then proceed to build a Skellam regression model that is able to predict the difference between the total demand and supply at a given station during a specific time period. Our experiments with real data further validate the accuracy of our proposed method.

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“…Double-Ended Queueing Models Double-ended queues have been studied for many applications, including taxi-service systems, perishableinventory systems, organ-transplant systems, and finance (cf. Kaspi and Perry 1983, Zenios 1999, Prabhakar et al 2000, Boxma et al 2011, Afeche et al 2014, and He et al 2018. In particular, Kaspi and Perry (1983) studied a perishable-inventory system with Poisson arrivals for both supplies and demands.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of a Time-Varying Double-Ended Production Queueing Model

Lee

Liu

et al. 2021

Stochastic Systems

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Motivated by production systems with nonstationary stochastic demand, we study a double-ended queueing model having back orders and customer abandonment. One side of our model stores back orders, and the other side represents inventory. We assume first-come-first-served instantaneous fulfillment discipline. Our goal is to determine the optimal (nonstationary) production rate over a finite time horizon to minimize the costs incurred by the system. In addition to the inventory-related (holding and perishment) and demand-related (waiting and abandonment) costs, we consider a cost that penalizes rapid fluctuations of production rates. We develop a deterministic fluid-control problem (FCP) that serves as a performance lower bound for the original queueing-control problem (QCP). We further consider a high-volume system for which an upper bound of the gap between the optimal values of the QCP and FCP is characterized and construct an asymptotically optimal production rate for the QCP, under which the FCP lower bound is achieved asymptotically. Demonstrated by numerical examples, the proposed asymptotically optimal production rate successfully captures the time variability of the nonstationary demand.

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Beyond Rebalancing: Crowd-Sourcing and Geo-Fencing for Shared-Mobility Systems

Cited by 6 publications

References 16 publications

Optimal Control of a Time-Varying Double-Ended Production Queueing Model

Optimal Control of a Time-Varying Double-Ended Production Queueing Model

Excess demand prediction for bike sharing systems

Optimal Control of a Time-Varying Double-Ended Production Queueing Model

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