Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems

Yang, Yuanxuan; Heppenstall, Alison; Turner, Andy; Comber, Alexis

doi:10.1016/j.compenvurbsys.2020.101521

Cited by 73 publications

(33 citation statements)

References 32 publications

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“…Smaller values of L 2 reflect a higher similarity between traffic patterns during two different time windows. In our analysis we found L 2 (f 2,6 ,f 23,27 23,27 ) = 14.53, L 2 (f 7,8 ,f 2,6 ) = 19.28, which indicates that spatial traffic distribution in the weekdays of the last week is more similar to the first weekend than to the first weekdays. This is confirmed by the fact that the first This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Spatio-temporal Analysismentioning

confidence: 49%

“…With this notation, the vectors associated to the weekdays of the first week, the first weekend, and the weekdays of the last week aref 2,6 ,f 7,8 , andf 23,27 , respectively. In order to evaluate the traffic "dissimilarity" between two considered periods, denoted with L 2 (f k,n ,f k ,n ), we simply look at the Euclidean norm of the difference vectors, that is L 2 (f k,n ,f k ,n ) = f k,n −f k ,n 2 .…”

Section: Spatio-temporal Analysismentioning

confidence: 99%

“…Dock-based bike sharing is particularly suited to this approach [26], since the trips are constrained to start and end at the docking stations, which hence represent fixed nodes in the connectivity graph. The use of graph analysis in bike sharing system evaluation and optimization is a growing field of research [27]. The…”

Section: Connectivity Analysismentioning

confidence: 99%

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Bike Sharing and Urban Mobility in a Post-Pandemic World

et al. 2020

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The Covid-19 pandemic has abruptly changed well-established mobility patterns, as the need for social distancing and lockdown orders have driven citizens to reduce their movements and avoid crowded mass transit. In this context, we look at the case of New York City's bike sharing system, one of the largest in the world, to gain insights on the socioeconomic variables behind urban mobility during a pandemic. We exploit several sources of Smart City data to analyze the relationship between bike sharing, public transport, and other modes of transportation, deriving interesting insights for future urban planning, both city-wide and at the neighborhood level. The New York City case study shows some of the most important trends during the lockdown, and the combination between mobility and socioeconomic data can be used to understand the consequences of the pandemic on different communities, as well as the future directions of expansion and management of the bike sharing system and urban infrastructure.

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Section: Spatio-temporal Analysismentioning

confidence: 49%

Section: Spatio-temporal Analysismentioning

confidence: 99%

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Bike Sharing and Urban Mobility in a Post-Pandemic World

et al. 2020

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“…Their results show the existence of a seasonal component in the demand for bike-sharing transportation and a declining trend in the use of taxi services. Also in the context of urban mobility, Yang et al [97] propose the utilization of graph-based features and deep neural networks to forecast demand patterns in the short term, thus supporting a more efficient organization of bike-sharing systems. Yet related to bike-sharing systems, Zhou et al [98] propose the use of random forest classification to support managers' decision making on the appropriate number of bicycles in each city area.…”

Section: Applications Of Machine Learning To Sustainable Transportation Systemsmentioning

confidence: 99%

Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

et al. 2021

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The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.

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“…Xiao et al [9] proposed a spatial–temporal GCNN to predict the station‐level departure and arrival of SBBS system in Wenling, China. Yang et al [10] identified graph‐based attributes and found that deep neural networks combined with these graph variables outperform other forecasting approaches. Guido et al [11] applied agglomerative hierarchical clustering to identify mobility patterns and predict city‐level demand.…”

Section: Introductionmentioning

confidence: 99%