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Changes to the collection of short walk data in the NTS
Glenn Goodman, DfT
• A walk less than one mile but greater than 50 yards in distance.
What is a short walk?
Timeline
Develop weighting
Short walks experiment
Further experiment
Public consultation
Consultation response
Cognitive testing
Decision on method
October 2015
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
Data collection
Travel week allocation
• Day 1 = Monday?• Day 7 = Sunday?
• Respondents allocated start day – uniform spread across week and month
Data collection
The importance of the data
• Walking & cycling policy• Transport forecasts and models
The decline of walking
234 trips
133 trips
58 trips
70 trips
Research outline
• Data are weighted to account for under-reporting – short walks only collected on day 7
Weighting for under-reporting
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
Timeline
Short walks experiment
April – June 2013
Experiment outline
• Aim: To assess day 1 vs. day 7• Sample size: 1,000
• Designed to detect a 5% point change
£££
2013 short walks experiment
Under-reporting: adults
Under-reporting: children
Under-reporting: type of walk
A B
Short walk only trip
A B
Multi stage trip
Under-reporting: type of walk
Timeline
Public consultation
July – Sept 2014
Consultation options
1
2
Day 7
Day 7
Another experiment
Produce weight
5
3
4
Day 1
Day 1 Produce weight
Break in series
Timeline
Consultationresponse
December 2014
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…”
3 4 Day 1
Timeline
Develop weighting
Ongoing
The approach to weighting
• Short walk only trips by adults• Logistic regression to calculate likelihoods• Determine explanatory variables• Produce weights
Developing back-series weight
• Age• Economic status• Car access
Example – car access
16%
7%
Developing back-series weight
• Age• Economic status• Car access• Gender – not significant
Example – gender
Developing back-series weight
• Age• Economic status• Car access• Gender• Household region• Settlement type (urban/rural)• Mobility• Income
– not significant– excluded
– excluded
– excluded– excluded
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
Logistic regression: Day 7
Day 7 No access
Main driver Other driver non-driver
Age 0-16 0.2 0.3
Full time 0.1 0.1 0.1 0.2
Part-time 0.2 0.2 0.2 0.3
Retired 0.1 0.1 0.1 0.2
Other non-work
0.2 0.2 0.2 0.3
With access
Logistic regression: Day 1
Day 1 No access
Main driver Other driver non-driver
Age 0-16 0.3 0.5
Full time 0.2 0.2 0.1 0.3
Part-time 0.2 0.3 0.2 0.4
Retired 0.2 0.3 0.2 0.3
Other non-work
0.2 0.3 0.2 0.4
With access
Relative likelihoods
No access
Main driver Other driver non-driver
Age 0-16 1.1 1.5
Full time 1.5 1.6 1.3 2.0
Part-time 1.4 1.5 1.2 1.7
Retired 1.7 1.8 1.5 2.2
Other non-work
1.3 1.4 1.2 1.6
With access
Day 7 weighted
Applying the weights
Day 7 unweighted
Day 1
Applying the weights
Historic likelihoods
Age 0-16
Adult, full-time
Adult, part-time
Adult, other non-work
Retired
Further work
• Cognitive interviews• Comparisons with other studies
London Travel Demand Survey Scottish Household Survey
• Further experiment
Timeline
Develop weighting
Short walks experiment
Further experiment
Public consultation
Consultation response
Cognitive testing
Decision on method
October 2015
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!
