Visualising where commuting cyclists travel using crowdsourced data

McArthur, David Philip; Hong, Jinhyun

doi:10.1016/j.jtrangeo.2018.11.018

Cited by 52 publications

(33 citation statements)

References 38 publications

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“…These three cycling routes are located near the city centre and include segregated cycling lanes. This result is consistent with the findings from McArthur and Hong (2019).…”

Section: Resultssupporting

confidence: 93%

The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data

2019

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The benefits of cycling have been well established for several decades. It can improve public health and make cities more active and environmentally friendly. Due to the significant net benefits, many local governments in Scotland have promoted cycling. Glasgow City Council constructed four significant pieces of cycling infrastructure between 2013 and 2015, partly in preparation for the 2014 Commonwealth Games and partly to encourage cycling more generally. This required substantial capital investment. However, the effectiveness of these big new infrastructure investments has not been well examined, mostly due to data limitations. In this study, we utilised data from the activity tracking app Strava for the years 2013-2016 and fixed effects panel data regression models to examine whether the new cycling infrastructure has increased cycling volumes on these routes. Our results show that three of the infrastructure projects have a positive effect on the monthly total volume of cycling trips made by users of the app, with flows up by around 12% to 18%. Although this result is promising, it needs to be interpreted with care due to the characteristics of the data.

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“…These three cycling routes are located near the city centre and include segregated cycling lanes. This result is consistent with the findings from McArthur and Hong (2019).…”

Section: Resultssupporting

confidence: 93%

The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data

2019

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“…Alternatively, they provided post-trip geographical information through an application not necessarily associated with the study. This type of data are those typically deriving from the registration of users to platforms such as Twitter [50], Foursquare [51,52], Strava and Strava Metro [39,[53][54][55][56] or Waze, and generically to other applications that, through geolocation, process traffic data and information on transport systems, such as Openstreetmap [32,[57][58][59][60]. A third of the analyzed papers foresees the active participation of users in the use of GIS through the creation and modification of spatial data: in these cases, a WebGIS application, generally created for the purposes of the study, is used by participants to draw paths, areas and points of interest [61,62].…”

Section: Use Of Gis and Type Of Involvementmentioning

confidence: 99%

“…Research focused on: the building of comprehensive urban networks through VGI data [32,51]; road and traffic condition analysis through crowdsourcing [70][71][72][73]; analysis of vehicle behavior [74] and investigation of travel patterns [52,[75][76][77], with an extensive use of SoftGIS, an internet-based approach which relies on collecting, analyzing and delivering soft knowledge produced by the residents and other local actors in a certain area through interactive maps [29,41,[78][79][80][81]; urban connectivity assessment [82] and general analysis of urban public transport and sustainable mobility aspects through smartphones [37,[83][84][85][86][87][88]. Crowdsourcing techniques have been extensively used in studies related to non-motorized mobility, such as cyclist spatial patterns identification [39,53,54,56,58,71], their socio-economic analysis [89] and environmental related problems [55,90] or the identification of peculiar elements of the pedestrian network to improve the walking experience of vulnerable people [29,55,57]. PPGIS has also been used in the maritime field for the analysis of conflicts, social values and preferences in marine environments [63][64][65]…”

Section: Stage Of the Transport Decision-making Processmentioning

confidence: 99%

Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making

Giuffrida

Pira

Inturri

et al. 2019

IJGI

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Transport decision-making problems are typically spatially based and involve a set of feasible alternatives with multiple evaluation criteria. Besides, transport decisions affect citizens’ quality of life, as well as specific interests of general stakeholders (e.g., transport companies), thus needing a participatory approach to decision-making. Geographic Information Systems (GIS) have the ability to visualize spatial data and represent the impact of location based transport alternatives, thus helping experts to conduct robust assessments. Moreover, with the recent diffusion of Volunteered Geographic Information (VGI) and development of Public Participatory GIS (PPGIS) platforms, the process can be enhanced thanks to the collection of a large amount of updated spatial data and the achievement of an active community participation. In this study, we provide an overview based on a structured literature review of the use of VGI and PPGIS in transport studies, exploring the fields of application, role played by GIS, level of public involvement and decision stage at which they are applied. From the overview’s results, we propose a general framework for the evaluation of transport alternatives using GIS from a multiple stakeholder point of view; the main conclusion is the usefulness of the integration between Public Participation, GIS and quantitative evaluation methods, in particular Multi Criteria Decision Analysis, in order to foster technically sound and shared decisions.

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“…Aside from anecdotal reports that Strava is mostly used by competitive cyclists, there are good reasons to doubt the representativeness of Strava users. For example, men seem to be heavily overrepresented among Strava users (Boss et al., 2018; McArthur and Hong, 2019; Watkins et al., 2016). However, differences in the demographic composition of the sample compared to the population will not necessarily result in different spatial patterns of cycling.…”

Section: Introductionmentioning

confidence: 99%

Predicting cycling volumes using crowdsourced activity data

Livingston

McArthur

Hong

et al. 2020

Environment and Planning B: Urban Analytics and City Science

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Planning for cycling is often made difficult by the lack of detailed information about when and where cycling takes place. Many have seen the arrival of new forms of data such as crowdsourced data as a potential saviour. One of the key challenges posed by these data forms is understanding how representative they are of the population. To address this challenge, a limited number of studies have compared crowdsourced cycling data to ground truth counts. In general, they have found a high correlation over the long run but with limited geographic coverage, and with counters placed on routes already known to be popular with cyclists. Little is known about the relationship between cyclists present in crowdsourced data and cyclists in manual counts over shorter periods of time and on non-arterial routes. We fill this gap by comparing multi-year crowdsourced data to manual cyclist counts from a cordon count in Scotland’s largest city, Glasgow. Using regression techniques, we estimate models that can be used to adjust the crowdsourced data to predict total cycling volumes. We find that the order of magnitude can be predicted but that the predictions lack the precision that may be required for some applications.

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Visualising where commuting cyclists travel using crowdsourced data

Cited by 52 publications

References 38 publications

The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data

The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data

Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making

Predicting cycling volumes using crowdsourced activity data

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