Using city-bike stopovers to reveal spatial patterns of urban attractiveness

Banet, Krystian; Kucharski, Rafał; Naumov, Vitalii

doi:10.1080/13683500.2021.2011841

Cited by 10 publications

(5 citation statements)

References 63 publications

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“…This form of transportation appeals particularly to environmentally mindful tourists in search of sustainable travel alternatives. Specifically, Banet et al [7] and Durán-Román et al [8] have confirmed the role of bike sharing in forming a destination's attractiveness considering its distinctive stopover feature. However, despite the insights provided by these studies, interest in bike sharing within the tourism domain remains relatively scarce.…”

Section: Introductionmentioning

confidence: 90%

Exploring Tourists’ Preferences for Bike-Sharing Services in the Context of Tourism

Tru,

Ngoc

2024

Sustainability

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This study explores tourists’ preferences for bike-sharing services in the context of tourism. Based on a sample of 800 individuals who visited Da Nang, Vietnam between July and August 2023, a latent class behavior model was developed to investigate preferences for bike-sharing services from tourists’ point of view. The results show that tourists prefer a bike-sharing service that ensures round-the-clock availability, is accessible within a 5-min walk from both the origin and destination, features bikes stationed at specific designated locations, and provides a variety of payment options at an affordable rate of USD 1.00 per h. Under these attributes, about 41.63% of tourists are likely to choose a bike-sharing option for their travel tours. These findings offer valuable insights for traffic management authorities and policymakers, demonstrating that bike-sharing services can be hailed as an effective complement to existing transportation modes and can help bridge the gap between supply and demand in tourist cities.

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Section: Introductionmentioning

confidence: 90%

Exploring Tourists’ Preferences for Bike-Sharing Services in the Context of Tourism

Tru,

Ngoc

2024

Sustainability

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“…Bike trips can be seen as mobile and social sensors to extract land usage patterns. It has been proven useful for identifying urban space attractiveness in [3], which is a good reference for improving city land planning and management. From another perspective, different land usage plans would also affect the usage of shared bikes (and other transit systems), and the prediction results should be taken into consideration for making decisions.…”

Section: Applications Beyond Bike Share Systemsmentioning

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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“…The traces of the shared bikes systems can be used to examine the bicyclists' behaviour in the built environment. For instance [28] used the traces of shared bike trips to reveal the spatial patterns of urban spaces attractiveness. Cargo bikes are other recent popular topic in this sub-category for transportation of last mile goods deliveries in urban areas [29,30].…”

Section: Knowledge Domains In Using Data-driven Approachesmentioning

confidence: 99%

Digital Bicycling Planning: A Systematic Literature Review of Data-Driven Approaches

et al. 2022

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To increase the amount of bicycling as a mode of transport, many countries are developing placed based bicycling plans and strategies. However, this approach necessitates considering a fine-scale mapping of bicycling patterns and a detailed description of urban spaces. The rise of new data and technologies offers much promise to planners and researchers to access diverse and richer sources of information to optimise the bicycling network design. This review aims to comprehensively examine the role of data and technology in bicycling planning, historical changes in using data-driven approaches, and current domains in the existing body of research in bicycling planning from 1990 to 2021. For this, a systematic literature review has been conducted according to PRISMA framework. A total number of 1022 studies was analysed and synthesised with the VOS Viewer and CiteSpace platforms. Upon completing the review, we extracted the most-used datasets, tools, and methodologies. The results of the systematic review reveal three evolutionary phases in using data-driven approaches from 1990 to 1999, 2000 to 2009, and 2010 to 2021. In addition, we identified six knowledge domains in using data-driven approaches in bicycling planning that is (i) smart city, (ii) infrastructure, (iii) built environment, (iv) decision making, (v) people, and (vi) safety.

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Using city-bike stopovers to reveal spatial patterns of urban attractiveness

Cited by 10 publications

References 63 publications

Exploring Tourists’ Preferences for Bike-Sharing Services in the Context of Tourism

Exploring Tourists’ Preferences for Bike-Sharing Services in the Context of Tourism

Bike sharing usage prediction with deep learning: a survey

Digital Bicycling Planning: A Systematic Literature Review of Data-Driven Approaches

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