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Big Data and Cycling

Author

Listed:
  • Gustavo Romanillos
  • Martin Zaltz Austwick
  • Dick Ettema
  • Joost De Kruijf

Abstract

Big Data has begun to create significant impacts in urban and transport planning. This paper covers the explosion in data-driven research on cycling, most of which has occurred in the last ten years. We review the techniques, objectives and findings of a growing number of studies we have classified into three groups according to the nature of the data they are based on: GPS data (spatio-temporal data collected using the global positioning system (GPS)), live point data and journey data. We discuss the movement from small-scale GPS studies to the ‘Big GPS’ data sets held by fitness and leisure apps or specific cycling initiatives, the impact of Bike Share Programmes (BSP) on the availability of timely point data and the potential of historical journey data for trend analysis and pattern recognition. We conclude by pointing towards the possible new insights through combining these data sets with each other -- and with more conventional health, socio-demographic or transport data.

Suggested Citation

  • Gustavo Romanillos & Martin Zaltz Austwick & Dick Ettema & Joost De Kruijf, 2016. "Big Data and Cycling," Transport Reviews, Taylor & Francis Journals, vol. 36(1), pages 114-133, January.
    • Handle: RePEc:taf:transr:v:36:y:2016:i:1:p:114-133
    DOI: 10.1080/01441647.2015.1084067
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    Citations

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    Cited by:

    1. Raturi, Varun & Hong, Jinhyun & McArthur, David Philip & Livingston, Mark, 2021. "The impact of privacy protection measures on the utility of crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 92(C).
    2. Radzimski, Adam & Dzięcielski, Michał, 2021. "Exploring the relationship between bike-sharing and public transport in Poznań, Poland," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 189-202.
    3. Scott N Lieske & Simone Z Leao & Lindsey Conrow & Chris Pettit, 2021. "Assessing geographical representativeness of crowdsourced urban mobility data: An empirical investigation of Australian bicycling," Environment and Planning B, , vol. 48(4), pages 775-792, May.
    4. Md Mintu Miah & Kate Kyung Hyun & Stephen P. Mattingly & Hannan Khan, 2023. "Estimation of daily bicycle traffic using machine and deep learning techniques," Transportation, Springer, vol. 50(5), pages 1631-1684, October.
    5. Pritchard, Ray & Bucher, Dominik & Frøyen, Yngve, 2019. "Does new bicycle infrastructure result in new or rerouted bicyclists? A longitudinal GPS study in Oslo," Journal of Transport Geography, Elsevier, vol. 77(C), pages 113-125.
    6. McArthur, David Philip & Hong, Jinhyun, 2019. "Visualising where commuting cyclists travel using crowdsourced data," Journal of Transport Geography, Elsevier, vol. 74(C), pages 233-241.
    7. Xie, Xiao-Feng & Wang, Zunjing Jenipher, 2018. "Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area," Journal of Transport Geography, Elsevier, vol. 71(C), pages 84-102.
    8. Qiao‐Chu He & Tiantian Nie & Yun Yang & Zuo‐Jun Shen, 2021. "Beyond Repositioning: Crowd‐Sourcing and Geo‐Fencing for Shared‐Mobility Systems," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3448-3466, October.
    9. Schimohr, Katja & Scheiner, Joachim, 2021. "Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption," Journal of Transport Geography, Elsevier, vol. 92(C).
    10. Petter Arnesen & Olav Kåre Malmin & Erlend Dahl, 2020. "A forward Markov model for predicting bicycle speed," Transportation, Springer, vol. 47(5), pages 2415-2437, October.
    11. Hochmair, Hartwig H. & Bardin, Eric & Ahmouda, Ahmed, 2019. "Estimating bicycle trip volume for Miami-Dade county from Strava tracking data," Journal of Transport Geography, Elsevier, vol. 75(C), pages 58-69.
    12. Jill Walker Rettberg, 2020. "Situated data analysis: a new method for analysing encoded power relationships in social media platforms and apps," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.

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