Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems

Shu, Jia; Chou, Mabel C.; Liu, Qizhang; Teo, Chung‐Piaw; Wang, I-Lin

doi:10.1287/opre.2013.1215

Cited by 226 publications

(109 citation statements)

References 12 publications

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“…In addition to this, some Sustainability 2017, 9, 895 4 of 21 researchers have focused on the different aspects of bicycle sharing system operations. For example, Shu et al [19] developed a network flow model to support decision-making in the design and management of the BSS. Li and Shan [20] provided a bidirectional incentive model to encourage the behavior of crowdsourcing to improve the performance of bicycle reposition.…”

Section: Literature Reviewmentioning

confidence: 99%

A Dynamic Programming Model for Operation Decision-Making in Bicycle Sharing Systems under a Sustainable Development Perspective

Shan

Liu

et al. 2017

Sustainability

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Abstract:Maintaining a balance between revenue and expenditure is the key to the sustainable development of a bicycle sharing system (BSS), and is a challenge for almost all systems worldwide. This article proposes a dynamic programming approach to obtain the optimal strategy to maximize the revenue of overall BSS. The Variable Granularity-Depth First Search (VG-DFS) algorithm is designed to speed up the solution. A numerical experiment is presented to verify the applicability of the model through a comparison with real data from the BSS in Hangzhou. Results indicated that the BSS could achieve break-even, or even obtain a substantial income by utilizing our model to make operational decisions, especially when the region it is located in has a relatively high GDP. Moreover, the best investment strategy proved is to involve stations in the initial construction period of the BSS as much as possible.

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Section: Literature Reviewmentioning

confidence: 99%

A Dynamic Programming Model for Operation Decision-Making in Bicycle Sharing Systems under a Sustainable Development Perspective

Shan

Liu

et al. 2017

Sustainability

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“…Those works assume there is only one fixed redeployment of bikes that happens at the end of the day. In contrast, [15] predict the stochastic demand from user trip data of Singapore metro system using poisson distribution and provide an optimization model that suggests the best location of the stations and a dynamic bicycles redeployment for the model to minimize the number of unsatisfied customers. However, they assume that redeployment of bikes from one station to another is always possible without considering the routing of carriers, which is a major cost driver for the bike-sharing company.…”

Section: Related Workmentioning

confidence: 99%

“…In order to generate different instances, we assumed the demand follows a poisson distribution as in the model provided by [15]. We divided each day in 4 time slots (from 8 : 00 up to 24 : 00).…”

Section: Data Preparationmentioning

confidence: 99%

On-line Dynamic Station Redeployments in Bike-Sharing Systems

Manna

2016

AI*IA 2016 Advances in Artificial Intelligence

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Abstract. Bike-sharing has seen great development during recent years, both in Europe and globally. However, these systems are far from perfect. The uncertainty of the customer demand often leads to an unbalanced distribution of bicycles over the time and space (congestion and/or starvation), resulting both in a loss of customers and a poor customer experience. In order to improve those aspects, we propose a dynamic bike-sharing system, which combines the standard fixed base stations with movable stations (using trucks), which will able to be dynamically re-allocated according to the upcoming forecasted customer demand during the day in real-time. The purpose of this paper is to investigate whether using moveable stations in designing the bike-sharing system has a significant positive effect on the system performance. To that end, we contribute an on-line stochastic optimization formulation to address the redeployment of the moveable stations during the day, to better match the upcoming customer demand. Finally, we demonstrate the utility of our approach with numerical experiments using data provided by bike-sharing companies.

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“…A MIP formulation considers an imaginary hub station to reduce the complexity that relocation operation involves in the MIP. Regarding tactical planning without anticipation of operational decisions, different approaches are presented by George and Xia (2011);Raviv and Kolka (2013); Cepolina and Farina (2012); Schuijbroek et al (2013); Shu et al (2013). Recent researches do not sufficiently cover the integration of the tactical and operational planning level.…”

Section: Service Network Design Of Bike Sharing Systemsmentioning

confidence: 99%

Anticipatory Service Network Design of Bike Sharing Systems

Neumann-Saavedra

Vogel

Mattfeld

2015

Transportation Research Procedia

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Today's conurbations suffer from inefficiency in transportation systems. Bike sharing systems (BSS) combine the advantages of public and private transportation to better exploit the given transportation infrastructure. They provide bikes for short-term trips at automated rental stations. However, spatio-temporal variation of bike rentals leads to imbalances in the distribution of bikes, causing full or empty stations in the course of the day. Ensuring the reliable provision of bike and free bike racks is crucial for the viability of these systems. On the tactical planning level, target fill levels of bikes at stations are determined to provide reliability in service. On the operational planning level, the BSS operator relocates bikes in vehicles among stations based on target fill levels. A recent approach in tactical service network design (SND) anticipates relocation operations of BSS by means of a dynamic transportation model yielding the required demand of relocation services (RS). A RS is described by pickup and return station, time period, and the number of relocated bikes. RS represent the design decision for implementing a service between two stations in each period for each day of the system operation. The output of the SND model are the time-dependent target fill levels at stations and the set of cost-efficient RS to facilitate these target fill levels. However, the existing approach neglects the sequence of RS into tours, thus leading to a weak anticipation of operational decisions. We extend an existing SND approach by including the concept of service tours (ST). RS are sequenced in ST which start and end at the depot. Experiments shows that the ST obtained by the extended SND yield a stronger anticipation of operational decisions.

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Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems

Cited by 226 publications

References 12 publications

A Dynamic Programming Model for Operation Decision-Making in Bicycle Sharing Systems under a Sustainable Development Perspective

A Dynamic Programming Model for Operation Decision-Making in Bicycle Sharing Systems under a Sustainable Development Perspective

On-line Dynamic Station Redeployments in Bike-Sharing Systems

Anticipatory Service Network Design of Bike Sharing Systems

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