7 References

Department for Transport. 2016. “National Propensity to Cycle Tool Project: Full Report with Annexes.” Active Travel. Predicting the Demand for Cycling. Department for Transport. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/510268/national-propensity-to-cycle-full-report.pdf.

Highways England. 2016. “Interim Advice Note 195/16: Cycle Traffic and the Strategic Road Network.” Interim 195/16. Highways England. http://www.standardsforhighways.co.uk/ha/standards/ians/pdfs/ian195.pdf.

Lovelace, Robin, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, and James Woodcock. 2017. “The Propensity to Cycle Tool: An Open Source Online System for Sustainable Transport Planning.” Journal of Transport and Land Use 10 (1). doi:10.5198/jtlu.2016.862.

Marqu’es, R., V. Hern’andez-Herrador, M. Calvo-Salazar, and J.A. Garc’ia-Cebri’an. 2015. “How Infrastructure Can Promote Cycling in Cities: Lessons from Seville.” Research in Transportation Economics 53 (November): 31–44. doi:10.1016/j.retrec.2015.10.017.

Padgham, Mark, Robin Lovelace, Maëlle Salmon, and Bob Rudis. 2017. “Osmdata.” The Journal of Open Source Software 2 (14). doi:10.21105/joss.00305.

Pucher, John, and Ralph Buehler. 2008. “Making Cycling Irresistible: Lessons from the Netherlands, Denmark and Germany.” Transport Reviews 28: 495–528. http://www.vtpi.org/irresistible.pdf.

R Core Team. 2017. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Transport for quality of life. 2016. “Typical Costs of Cycling Interventions: Interim Analysis of Cycle City Ambition Schemes.” Interim SO17265. Department for Transport.