copyright © 2004 bolton institute john parkin principal lecturer bolton institute determination and...

Copyright © 2004 Bolton InstituteCopyright © 2004 Bolton Institute

John ParkinPrincipal Lecturer

Bolton Institute

Determination and measurement of factors which influence propensity to cycle

to work


District level variation incycle use –

1991

Proportion using the bicycle for the journey to work from the 1991 census


Overall census journey to work proportion by bicycle

1981 1991 2001

England 4.11% 3.21% 3.11%

Wales 1.59% 1.41% 1.53%

Scotland 1.44% 1.36% 1.53%

Great Britain

3.76% 2.97% 2.89%


Districts with more than 8% cycle mode share

2001 District Name and Code 2001 % cyclists 1991 % cyclists 1981 % cyclists

12UB Cambridge 28.34% 26.06% 27.61%

38UC Oxford 16.22% 16.26% 20.25%

15UH Isles of Scilly 15.59% 15.04% 6.58%

00FF York UA 13.06% 17.93% 20.98%

00FA Kingston upon Hull, UA 12.32% 12.69% 15.07%

24UF Gosport 11.44% 14.45% 14.70%

32UB Boston 11.13% 14.31% 18.91%

33UG Norwich 9.37% 9.75% 12.98%

42UH Waveney 9.27% 11.54% 16.60%

00JA Peterborough UA 8.33% 10.76% 13.83%

00FC North East Lincolnshire UA 8.19% 8.54% 11.84%


Districts with 1% point increase or more

2001 District Name and Code 2001 % cyclists

1991 % cyclists

1981 % cyclists

2001-1991

00AM Hackney 6.83% 4.03% 2.56% 2.81%

12UB Cambridge 28.34% 26.06% 27.61% 2.28%

00HB Bristol; City of UA 4.94% 3.30% 3.21% 1.64%

00AU Islington 5.15% 3.52% 2.59% 1.63%

00MC Reading UA 4.44% 2.83% 3.98% 1.61%

00AN Hammersmith and Fulham 5.21% 3.80% 3.89% 1.41%

00AY Lambeth 4.47% 3.06% 2.49% 1.41%

18UC Exeter 4.84% 3.44% 4.07% 1.40%

00AG Camden 4.10% 2.78% 2.52% 1.32%

39UD Oswestry 2.94% 1.75% 5.94% 1.19%

00ML Brighton and Hove UA 2.97% 1.82% 1.53% 1.15%

00BJ Wandsworth 4.22% 3.07% 3.12% 1.14%

00BE Southwark 3.98% 2.89% 2.21% 1.10%

00FY Nottingham UA 3.93% 2.93% 2.96% 1.00%


Waldman’s research

• Waldman (1977) undertook the first aggregate analysis of cycle trip making.

• He demonstrated that hilliness and danger are important factors.

Type of borough

Predicted cycle level

Example boroughs

Actual cycle levels

HILLY and

SAFE4% Matlock

Worsley

Bodmin

4%

4%

6%

FLAT and

DANGEROUS6% Hammersmith

Liverpool

Barking

5%

3%

9%

HILLY and DANGEROUS

0% Sheffield

Plymouth

Burnley

1%

2%

2%

FLAT and

SAFE43% Goole

Newark

Cambridge

52%

42%

36%


Risk research studies

• Waldman recommended that measures be developed for accident risk that can be related to traffic and road features.

• Mathew (1995) used Waldman’s model: significant increases in cycling would occur if danger were halved.

• Landis et al. (1997) used responses from cyclists cirumnavigating a pre-determined circuit.

• Guthrie et al. (2001) undertook similar research in the UK to determine what were termed “cyclability” factors.


Overview of modelling

Risk rating survey (primary data)

Census, transport and physical factor data

(secondary data)

Non-linear regression analysis

of ratings data

Logit model analysis of ratings data using

threshold of acceptability

Analysis of 8850 wards with %JTWB as

dependent variable

Analysis of 1117 wards with %JTWB as

dependent variable


The video based technique

• “Real life” is time consuming and will limit the variety of environments exposed to respondents.

• Video has the following advantages:

1. Respondent senses movement in traffic.2. Respondent will feel and think from the cyclists’

point of view.3. Respondent will feel physically close to traffic.4. Respondent will look ahead and consider developing

road situation from cyclists’ point of view.


The survey instrumentThe principal questions asked of the respondents are:

1. How do you rate risk to you from traffic?

2. How do you rate threats to you from sources other than traffic?


Developments through the pilot

• A pre-pilot grouped 20 video clips into journeys comprising 4 clips each.

• Methodology abandoned: respondent memory problems.

• A pilot showed each of the 20 clips individually.

• Results of the correct order: BUT CANNOT BUILD ROUTES.


The full survey methodology

• Junctions represent the “punctuation marks” in the “sentence length” of the route; it is crucial to understand the relationship between the two.

• New methodology:1. Respondent’s home to work journey2. Vary this by addition and removal of junctions

and substitution of route lengths

5 mins 7 mins 15 mins

3 mins

Traffic lights, no facilities right turn J4

Rbt, with facilities straight on J5

Busy road into town –R7

Traffic calmed road R3

Residential road – R2 Town centre –

R8


Respondent characteristics (n=144)

Cyclist Type Cycling frequency

Description % Bands %

Can cycle but do not 61.8 Never cycle 35.4

Leisure cyclist 16.7 Occasional holiday times / weekends

38.9

Commuter / utility cyclist

8.3 1 – 3 times per month

5.6

Cycle tourist 2.1 1 – 2 times per week 6.9

Sports cyclist 0.0 2+ times per week 13.2

Mixture of types 11.1

Total 100.0 Total 100.0


Sources of variability in the data

• Clues from worded descriptions

• Scale points too coarse / linear:non-linear

• Too large a rating change on an addition/removal/substitution

• Not rating the clips they were viewing and assuming different traffic conditions

• Obtuse / tired / too difficult or other bias

• Risk Rating versus Personal Security Rating indistinct


Rating scale response shapes

0

5

10

Z

RR

0

5

10

Z

RR

0

5

10

Z

RR

0

5

10

Z

RR

0

5

10

Z

RR

RR

0

5

10

Z

RR

Linear Logisitc Gompertz

Asymptotic to 10 Asymptotic to 1 Weibull


Non-linear modelling

• Phase 1: investigate response shapes using dichotomous variables for presence of route or junction

• Phase 2: add in variable for time on route and number of junctions traversed

• Phase 3: investigate and eliminate spurious respondents and variables

• Phase 4: add in variables for cyclist regularity, sex and age

• Phase 5: further modelling with Gompertz looking at interaction effects


Some overall findings

• Respondents rated traffic free situations as less risky, roundabouts add risk, not true for signals

• Regularity of cycling, age and sex interact with journey variables

• Threats from sources other than traffic included:

Pedestrians, children, parked cars, animals, gully gratings, other ironworks and potholes and the state of the road.


Main Model functional form

Model of the form: )1( iZe

SRR

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Saturation level at 100%

Saturation level at 45%


Main Model functional form

%JTWB = fn {age,

car ownership,SEC,ethnicity,distance,highway defects score,network density,hilliness,rainfall,mean temperature}


So what….? and where to…..?

• Rating scale use is complex, essential to understand.

• Individual’s threshold levels are a useful guide to acceptability of cycling to create a district wide measure.

• Many complex interactions of person type and journey type.

• More work on the main model (8850 wards) and the main sub-model (1117 wards) using the outcome from the acceptability of cycling model


John ParkinBolton Institute

[email protected]

copyright © 2004 bolton institute john parkin principal lecturer bolton institute determination and...

Documents

bolton institute districts

census slide

dependent variable slide

dependent variable analysis

cyclists cirumnavigating

cycle mode share

ub cambridge28

used waldmans model