1. Changes to the collection of short walk data in the NTS Glenn Goodman, DfT

2. A walk less than one mile but greater than 50 yards in distance. What is a short walk?

3. Timeline Develop weighting Short walks experiment Further experiment Public consultation Consultation response Cognitive testing Decision on method October 2015

4. Background: Household survey of personal travel First survey in 1965. Continuous since 1988 2013: n = 7k households, 16k people (63% resp. rate) Complex design – stratified two-stage random sample

5. Data collection

6. Travel week allocation Day 1 = Monday? Day 7 = Sunday? Respondents allocated start day – uniform spread across week and month

7. Data collection

8. The importance of the data Walking & cycling policy Transport forecasts and models

9. The decline of walking 234 trips 133 trips 58 trips 70 trips

10. Research outline Data are weighted to account for under- reporting – short walks only collected on day 7

11. Weighting for under-reporting

12. Research question Data are weighted to account for under- reporting – short walks only collected on day 7 Aims: To assess whether under-reporting of short walk trips exists If so, correct this under-reporting

13. Timeline Short walks experiment April – June 2013

14. Experiment outline Aim: To assess day 1 vs. day 7 Sample size: 1,000 Designed to detect a 5% point change £££

15. 2013 short walks experiment

16. Under-reporting: adults

17. Under-reporting: children 11% 1.5%

18. Under-reporting: type of walk A B Short walk only trip AB Multi stage trip

19. Under-reporting: type of walk 1.1%

20. Timeline Short walks experiment April – June 2013 Public consultation July – Sept 2014

21. Consultation options 1 2 Day 7 Another experiment Produce weight 53 4 Day 1 Produce weight Break in series

22. Timeline Public consultation July – Sept 2014 Consultation response December 2014

23. Consultation response “…I would want to be very sure before making irreversible modifications to NTS protocols…the NTS has weaknesses and biases that are essentially stationary…turbulent biases are much worse…” 34 Day 1

24. Timeline Consultation response December 2014 Develop weighting Ongoing

25. The approach to weighting Short walk only trips by adults Logistic regression to calculate likelihoods Determine explanatory variables Produce weights

26. Developing back-series weight Age Economic status Car access

27. Example – car access 16% 7%

28. Developing back-series weight Age Economic status Car access Gender – not significant

29. Example – gender

30. Developing back-series weight Age Economic status Car access Gender Household region Settlement type (urban/rural) Mobility Income – not significant – excluded

31. Developing back-series weight Age + economic status  0-16  Full time  Part-time  Retired  Other non-work Car access  Main driver  Other driver  Non-driver  No access

32. Logistic regression: Day 7

33. Logistic regression: Day 1

34. Relative likelihoods

35. Day 7 weighted Applying the weights Day 7 unweighted Day 1

36. Applying the weights

37. Historic likelihoods Age 0-16 Adult, full-time Adult, part-time Adult, other non-work Retired

38. Further work Cognitive interviews Comparisons with other studies  London Travel Demand Survey  Scottish Household Survey Further experiment

39. Timeline Develop weighting Short walks experiment Further experiment Public consultation Consultation response Cognitive testing Decision on method October 2015

40. Conclusion Experience of formal consultation process Highlights the value of ONS Methodology Advisory Service Lessons for other household surveys Better data on walking for the future!