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Estimating distance decay for the National Propensity to Cycle Tool

67b1027cca3877a76a9024425519ddde?s=47 Robin
June 11, 2015

Estimating distance decay for the National Propensity to Cycle Tool

Seminar talk delivered at the ITS



June 11, 2015


  1. Estimating distance decay: for the National Propensity to Cycle Tool

    (NPCT) Robin Lovelace, University of Leeds. Talk at the Institute for Transport Studies (ITS) Slides: speakerdeck.com/robinlovelace
  2. I What is distance decay?

  3. Discovering distance decay Source: Lovelace (2014): my thesis

  4. Distance of trip != distance/yr Source: Lovelace (2014): my thesis

  5. Distance decay in active travel Data: derived from Iacono et

    el. (2010)
  6. Distance decay: the formula p = f(d)

  7. Euclidean or route distance? Source: Lovelace (2014): my thesis

  8. Distance decay in the literature Source: Lovelace et al., forthecoming.

  9. Tested functional forms Source: Martínez and Viegas (2013)

  10. DD functional forms

  11. Exponential vs cubic vs linear

  12. Some real data Source: Lovelace et al. (forthcoming)

  13. Linear model to the data

  14. Cubic polynomial models

  15. Log-square-root model

  16. II Why distance decay? Source: NPCT model Output (Leeds)

  17. Distance decay in everyday life Source: Lovelace (2014): my thesis

  18. Distance and energy use

  19. Distance and mode dependence (Sheffield)

  20. Distance and mode dependence (England) Source: Lovelace (2014): my thesis

  21. Energy and travel mode Source: Lovelace (2014): my thesis

  22. III The uses of distance decay

  23. Uses of dd: a few ideas • If we're to

    transition away from fossil fuels, we must use modes that are less energy intensive • Distance decay for sustainable travel modes can tell us what future travel patterns must look like • Characterising travel systems • Especially relevant for active travel • The National Propensity to Cycle Tool (NPCT)
  24. 'Spatial interaction models' Source: Lovelace et al. (forthecoming), Geographical Analysis

  25. DD to characterise travel systems

  26. Modelling travel Source: Lovelace et al. (2014)

  27. IV Wider context of distance decay

  28. Resulting report: national benefits

  29. Benefits of cycling nationwide See https://tinyurl.com/conversation-cycling

  30. Webtag estimates of benefit:cost See https://tinyurl.com/conversation-cycling https://www.gov.uk/government/publications/webtag- tag-overview

  31. An open source policy planning tool (+ live demo!)

  32. DD in the NPCT

  33. Further work on distance decay • The distance-decay of road

    traffic incidents? • Its use to estimate non-work travel? • Verification of dd parameters using Big Data
  34. Key references • Iacono, M., Krizek, K. J., & El-Geneidy,

    A. (2010). Measuring non-motorized accessibility: issues, alternatives, and execution. Journal of Transport Geography, 18(1), 133–140. doi:10.1016/j.jtrangeo.2009.02.002 • Lovelace, R., Clarke, M., Cross, P., & Birkin, M. (n.d.). Verification of big data for estimating retail flows: a comparison of three sources against model results. Geographical Analysis. • Lovelace, R., Malleson, N., Harland, K., & Birkin, M. (2014). Geotagged tweets to inform a spatial interaction model: a case study of museums. Arxiv Working Paper. • Lovelace, R. (2014). The energy costs of commuting: a spatial microsimulation approach. University of Sheffield. Retrieved from http://etheses.whiterose.ac.uk/5027/ • Martínez, L. M., & Viegas, J. M. (2013). A new approach to modelling distance- decay functions for accessibility assessment in transport studies. Journal of Transport Geography, 26, 87–96. doi:10.1016/j.jtrangeo.2012.08.018