Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa

2 references·16 citing·58 second hopsampled

FromToDepth

this work references cited by second hop· second hop is a sample of the most-cited connections

References

2012Where do cyclists ride? A route choice model developed with revealed preference GPS data693 cited
2001The Built Environment and Human Activity Patterns: Exploring the Impacts of Urban Form on Public Health593 cited

Cited by

2019Factors influencing park popularity for mountain bikers, walkers and runners as indicated by social media route data85 cited
2018Revealed Preference Methods for Studying Bicycle Route Choice—A Systematic Review59 cited
2020Lessons from Implementing a Metropolitan Electric Bike Sharing System55 cited
2019Exploring the influence of road network structure on the spatial behaviour of cyclists using crowdsourced data40 cited
2020Modeling bicycle volume using crowdsourced data from Strava smartphone application33 cited
2020Digital Platform/Mobile App to Boost Cycling for the Promotion of Sustainable Mobility in Mid-Sized Starter Cycling Cities27 cited
2023Revealing the determinants of gender inequality in urban cycling with large-scale data26 cited
2021Modelling cyclists’ route choice using Strava and OSMnx: A case study of the City of Glasgow24 cited
2020From shared micro-mobility to shared responsibility: Using crowdsourcing to understand dockless vehicle violations in Austin, Texas24 cited
2021Spatial Integration of Non-Motorized Transport and Urban Public Transport Infrastructure: A Case of Johannesburg20 cited
2017Impact of Urban Policy on Public Transportation in Gauteng, South Africa: Smart or Dumb City Systems Is the Question

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

19 cited

2022Exploring Data Fusion Techniques to Estimate Network-Wide Bicycle Volumes19 cited

2020Bicycle Ridership Using Crowdsourced Data: Ordered Probit Model Approach17 cited

2022Cycling in sub-Saharan African cities: Differences and similarities with developed world cities17 cited

2022Cycling in Lagos: The challenges, opportunities, and prospects16 cited

2022Leveraging the spatial-temporal resolution of crowdsourced cycling data to improve the estimation of hourly bicycle volume14 cited