Bike-sharing stations: A maximal covering location approach

Frade, Inês; Ribeiro, Anabela

doi:10.1016/j.tra.2015.09.014

Cited by 142 publications

(85 citation statements)

References 16 publications

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“…An efficient shared bicycle system includes stations in areas of greater demand, pondering a trade-off between cost and benefits of each station. A combinatorial optimization problem of placing such stations along the cities is crucial [76], being advocated as a crucial tool for helping urban managers. Other optimization problems were dealt in order to determine the optimal number of bikes in each station [77], most of them handling stochastic variables and modeling.…”

Section: Mobility and Citizensmentioning

confidence: 99%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes.

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Section: Mobility and Citizensmentioning

confidence: 99%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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“…Church and ReVelle [21] also introduced the maximal covering models to maximize the demand coverage. These models and their applications were widely used for determining the location of BSSs stations based on the maximization of covered demand [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours

Sun

Zuo

2019

Journal of Advanced Transportation

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In recent years, free-floating bike-sharing systems (FFBSSs) have been considerably developed in China. As there is no requirement to construct bike stations, this system can substantially reduce the cost when compared to the traditional bike-sharing systems. However, FFBSSs have also become a critical cause of parking disorder, especially during the morning and evening rush hours. To address this issue, the local governments stipulated that FFBSSs are required to deploy virtual stations near public transit stations and major establishments. Therefore, the location assignment of virtual stations is sufficiently considered in the FFBSSs, which is required to solve the parking disorder and satisfy the user demand, simultaneously. The purpose of this study is to optimize the location assignment of virtual stations that can meet the growing demand of users by analyzing the usage data of their shared bikes. This optimization problem is generally formulated as a mixed-integer linear programming (MILP) model to maximize the user demand. As an alternative solution, this article proposes a clustering algorithm, which can solve this problem in real time. The experimental results demonstrate that the MILP model and the proposed method are superior to the K-means method. Our method not only provides a solution for maximizing the user demand but also gives an optimized design scheme of the FFBSSs that represents the characteristics of virtual stations.

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“…Each location has a capacity equal to 15 and a buffer area (area of influence) with a radius of 100 m (we need these values for the BEI calculation). The ratio between the number of racks and the number of bicycles in the system has been set equal to 1.25, according to Frade and Ribeiro [38].…”

Section: Sioux Falls City: Network Description and Setupmentioning

confidence: 99%

Planning and Design of Equitable Free-Floating Bike-Sharing Systems Implementing a Road Pricing Strategy

Caggiani

Camporeale

Ottomanelli

2017

Journal of Advanced Transportation

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Promoting a more sustainable development of urban realities is one of the most important goals of the recent decades. One possible strategy to undertake in order to achieve this objective is the implementation of a road pricing: tolling private cars when passing by certain roads of the network could be a way to tone down the traffic congestion and, at the same time, encourage the shifting towards more sustainable means of transport. In this context, we suggest a method to distribute in a fair way the outcomes/revenues of this pricing strategy. In particular, we propose to design a free-floating bike-sharing system whose resources could be allocated in the territory according to spatial and social equity principles. The relation between the amount of the tolls, the number of days of application of the policy, and the pursued equity is investigated, and both a numerical application (to a test network and to a real case study) and a sensitivity analysis in support of the method are enclosed.

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Bike-sharing stations: A maximal covering location approach

Cited by 142 publications

References 16 publications

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours

Planning and Design of Equitable Free-Floating Bike-Sharing Systems Implementing a Road Pricing Strategy

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