changes to the collection of short walk data in the nts glenn goodman, dft

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!