Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review

Albuquerque, Vitória; Dias, Miguel Sales; Bação, Fernando

doi:10.3390/ijgi10020062

Cited by 35 publications

(17 citation statements)

References 40 publications

(88 reference statements)

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“…Nevertheless, some other distinct approaches outside the clustering sphere have been developed in the latest years, as is the example of another study regarding the mixed fleet biking system also in Lisbon [41]. In a more generalized view, a survey presented regarding the Machine Learning (ML) applications to a bike-sharing system [42] was also considered, presenting some of the most relevant applications in this field.…”

Section: Related Workmentioning

confidence: 99%

A Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study

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Arantes²,

Figueiredo³

et al. 2022

Smart Cities

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Urban mobility is a massive issue in the current century, being widely promoted the need of adopting sustainable solutions regarding transportation within large urban centres. The evolution of technologies has democratised smart cities to better plan and manage their mobility solutions, without compromising the social, economic, and environmental impacts. Pursuing the carbon neutrality and the climate agreement goals, soft mobility is one of the most popular emerging methods to provide greener alternatives regarding mobility. Among these transportation modes are the bicycle, which has been widely used in several public systems across the world, one of them being in Lisbon. This article provides a decision support system for bike-sharing docking stations for three council parishes of the city, namely, Parque das Nações, Marvila, and Beato. Taking advantage of clustering methods and GSM data from a telecommunication operator, this study pretends to highlight a novel approach to identify soft mobility hotspots, in specific bike-sharing docking stations, for suited mobility management systems in Lisbon’s city centre.

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Section: Related Workmentioning

confidence: 99%

A Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study

Fontes

Arantes²,

Figueiredo³

et al. 2022

Smart Cities

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show abstract

“…We would like to thank Ricardo Pinto and Vitória Albuquerque [66] for their help reviewing the paper. The authors would also like to thank the reviewers and the editorial team who offered edifying and useful remarks to enhance the quality of the paper.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

2021

Self Cite

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The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.

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“…However, none of them focus on bike share systems. Another recent survey [1] is concerned about the machine learning approaches used in bike share systems, but the focus is not usage prediction problems. Earlier studies applying deep learning techniques for bike sharing usage prediction were based only on docked bike share systems and did not incorporate dockless bike share systems [39].…”

Section: Introductionmentioning

confidence: 99%

Bike sharing usage prediction with deep learning: a survey

Jiang

2022

Neural Comput & Applic

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As a representative of shared mobility, bike sharing has become a green and convenient way to travel in cities in recent years. Bike usage prediction becomes more important for supporting efficient operation and management in bike share systems as the basis of inventory management and bike rebalancing. The essential of usage prediction in bike sharing systems is to model the spatial interactions of nearby stations, the temporal dependence of demands, and the impacts of environmental and societal factors. Deep learning has shown a great advantage of making a precise prediction for bike sharing usage. Recurrent neural networks capture the temporal dependence with the memory cell and gate mechanisms. Convolutional neural networks and graph neural networks learn spatial interactions of nearby stations with local convolutional operations defined for the grid-format and graph-format inputs respectively. In this survey, the latest studies about bike sharing usage prediction with deep learning are reviewed, with a classification for the prediction problems and models. Different applications based on bike usage prediction are discussed, both within and beyond bike share systems. Some research directions are pointed out to encourage future research. To the best of our knowledge, this paper is the first comprehensive survey that focuses on bike sharing usage prediction with deep learning techniques.

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Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review

Cited by 35 publications

References 40 publications

A Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study

A Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study

Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

Bike sharing usage prediction with deep learning: a survey

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