Generalized model for mapping bicycle ridership with crowdsourced data

Nelson, Trisalyn; Roy, Avipsa; Ferster, Colin; Fischer, Jaimy; Brum-Bastos, Vanessa; Laberee, Karen; Yu, Honghui; Winters, Meghan

doi:10.1016/j.trc.2021.102981

Cited by 35 publications

(22 citation statements)

References 38 publications

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“…Additionally, as described above, network characteristics may not reflect how connected and comfortable a network is for all ages and abilities. Further, we did not consider land use and other parameters shown to be associated with bicycle ridership, 7,34 as the primary focus of this study was on the association between network characteristics and bicycle ridership.…”

Section: Discussionmentioning

confidence: 99%

Association between network characteristics and bicycle ridership across a large metropolitan region

Beck¹,

Pettit²,

Winters³

et al. 2021

Preprint

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Background: Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership.Methods: We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated and Bayesian spatial models were used to explore the association between these network characteristics and bicycle ridership (measured as counts of the number of trips, and the proportion of all trips that were made by bike). Results: We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region.Conclusions: These findings challenge the utility of approaches based on spatially modelling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. There is a need to progress the science of measuring safe and connected bicycle networks for people of all ages and abilities.

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Section: Discussionmentioning

confidence: 99%

Association between network characteristics and bicycle ridership across a large metropolitan region

Beck¹,

Pettit²,

Winters³

et al. 2021

Preprint

Self Cite

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“…15,41 These studies have demonstrated the importance of bicycle network accessibility, connectivity and comfort, land use mix, population characteristics (including socioeconomic status) and topography have on bicycle ridership. 9,10,12,14,15 The vast majority of these prior studies have used various forms of regression modelling to explore associations between these built environment and population characteristics, and bicycle ridership, typically generating single coefficients to quantify the association. These approaches are limited in their ability to understand the interactions of built environment, population and bicycling characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…11,13,15,31 Other population and land use characteristics, such as population density, and land use mix, have also previously been demonstrated to be associated with bicycle ridership. 10,12,15 Similar to prior studies that have developed typologies/indices, 14,19 the final set of indicators chosen for inclusion in the urban biking typology reflected a balance of parsimony and pragmatism. We acknowledge that the included indicators do not capture the full complexity of bicycle ridership and urban form, but represent key attributes that have previously been associated with bicycle ridership.…”

Section: Methodsmentioning

confidence: 99%

“…8 Numerous studies have explored the association between bicycle network, population and land use characteristics, and measures of bicycle ridership (such as mode share). 2,[9][10][11][12][13][14][15] These studies have been important in quantifying the importance of built environment characteristics and connected bicycle networks in enabling bicycling. However, these approaches have been limited in their ability to quantify the complex interactions of these factors.…”

Section: Introductionmentioning

confidence: 99%

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Developing urban biking typologies: quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics

Beck¹,

Winters²,

Nelson³

et al. 2021

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Background: Extensive research has been conducted exploring associations of built environment characteristics and biking. However, these approaches have often lacked the ability to understanding the interactions of built environment, population and bicycle ridership. To overcome these limitations, this study aimed to develop novel urban biking typologies using unsupervised machine learning methods.Methods: We conducted a retrospective analysis of travel surveys, bicycle infrastructure and population and land use characteristics in the Greater Melbourne region, Australia. To develop the urban biking typology, we used a k-medoids clustering method.Results: Analyses revealed 5 clusters. We highlight areas with high bicycle network density and a high proportion of trips made by bike (Cluster 1; reflecting 12% of the population of Greater Melbourne, but 57% of all bike trips) and areas with high off-road and on-road bicycle network length, but a low proportion of trips made by bike (Cluster 4, reflecting 23% of the population of Greater Melbourne and 13% of all bike trips).Conclusion: Our novel approach to developing an urban biking typology enabled the exploration of the interaction of bicycle ridership, bicycle network, population and land use characteristics. Such approaches are important in advancing our understanding of bicycling behaviour, but further research is required to understand the generalisability of these findings to other settings.

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“…We recognize that issues and potential responses to the 3Es are a vast canvas. In GIScience a sample of the relevant topics includes ethics ( 36 – 38 ); data representation ( 39 ); justice, equity, diversity, and inclusion ( 40 – 44 ); location and privacy ( 45 ); inference from spatial data ( 46 ); provenance and uncertainty in data ( 47 ); teaching practices ( 48 , 49 ); and reproducibility and replicability ( 50 – 54 ). As we cannot give adequate attention to all topics, we highlight those that either require a unique response from GIScience or where leadership from GIScience will have broad impacts for science, academia, and society.…”

mentioning

confidence: 99%

Accelerating ethics, empathy, and equity in geographic information science

Nelson

Goodchild

Wright

2022

Proc. Natl. Acad. Sci. U.S.A.

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Significance There has been a groundswell in the support needed to center ethics, empathy, and equity in scientific thought and practice. Drawing on our experience from GIScience, our goal is to accelerate ethical, empathetic, and equitable scientific practices. Many of the opportunities and challenges we outline are broadly applicable and will stimulate the conversations needed to accelerate transformation of science practice and culture. With an emphasis on practical suggestions for reshaping science, we invite all scientists to join in a fundamentally different approach. This paper is a step toward mobilizing the scientific community toward ethics, empathy, and equity by inviting humility, broader measures of excellence and success, diversity in our networks, and the creation of pathways to inclusive education.

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Generalized model for mapping bicycle ridership with crowdsourced data

Cited by 35 publications

References 38 publications

Association between network characteristics and bicycle ridership across a large metropolitan region

Association between network characteristics and bicycle ridership across a large metropolitan region

Developing urban biking typologies: quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics

Accelerating ethics, empathy, and equity in geographic information science

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