A model is presented that relates the proportion of bicycle journeys to work for English and Welsh electoral wards to relevant socio-economic, transport and physical variables. A number of previous studies have exploited existing disaggregate data sets. This study uses UK 2001 census data, is based on a logistic regression model and provides complementary evidence based on aggregate data for the determinants of cycle choice. It suggests a saturation level for bicycle use of 43%. Smaller proportions cycle in wards with more females and higher car ownership. The physical condition of the highway, rainfall and temperature each have an effect on the proportion that cycles to work, but the most significant physical variable is hilliness. The proportion of bicycle route that is off-road is shown to be significant, although it displays a low elasticity (+0.049) and this contrasts with more significant changes usually forecast by models constructed from stated preference based data. Forecasting shows the trend in car ownership has a significant effect on cycle use and offsets the positive effect of the provision of off-road routes for cycle traffic but only in districts that are moderately hilly or hilly. The provision of infrastructure alone appears insufficient to engender higher levels of cycling.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
A district comprises a local authority area, and may be predominantly rural or urban in nature.
England comprises nine Government regions. Parts of Yorkshire and Humberside, the East Midlands and the East Region are generally very flat.
Wards are electoral units within a district with mean size of 17 ha. The 50th percentile ward population aged 16–74 is 3,469, with the 10th percentile and 90th percentiles being 1,402 and 8,660. The inter-quartile range is from 1,940 to 5,582. The census data for Scotland and Northern Ireland is collected and stored in different ways than for England and Wales using different geographical units. Data for the journey to work in Scotland include journeys for education for those aged 16 and over. Some of the explanatory variables are not available in the same form in Scotland and Northern Ireland as in England and Wales. For these reasons the study has been limited to England and Wales.
A Large User Postcode is one that has been assigned to a single address due to the large volume of mail received at that address. A Small User Postcode identifies a group of delivery points. On average there are 15 delivery points per Postcode, however this can vary between 1 and 100. There are 1.71 million postcodes in the UK. The travel to work distance is calculated to the nearest 1 km.
The mean slope for a kilometre square is determined by passing a 3 × 3 operator (grid) over a 20 × 20 matrix within each kilometre square column by column and row by row. The 3 × 3 operator determines the slope at the centre point of the matrix by calculating the change in slope in both orthogonal directions for the surrounding matrix points and then averaging.
A value of unity is ascribed to districts with no mapping data and zero otherwise.
Note that “higher professional occupations” cover all types of higher professional work , whether occupied by employers, the self-employed, or as an employee. The other sub-category within “higher managerial and professional occupations” is for large employers and higher managerial occupations, both of which are occupations usually within larger organisations than “higher professional occupations”.
Note that the difference in magnitude of the coefficient estimates results from the different scales used for England and Wales: when this is accounted for, income deprivation has approximately the same magnitude of effect in each country.
The elasticity is determined about the mean value for the variable with all other variables held at their mean values.
The mean hilliness value is 0.67 and a 10% change about this mean represents only relatively subtle changes in hilliness, for example moving from Camden in inner London to Watford on the outskirts of London, or equivalently in the Midlands, moving from Coventry to North Warwickshire.
The forecast increases in car ownership in Bradford, Doncaster, York and Merton between 2001 and 2011 are respectively 22%, 18%, 22% and 14%. Average trip distance was 6.6 miles in 1998/00, rising to 6.8 miles in 2002 and 6.9 miles in 2003, but then declining to 6.8 miles in 2004 (National Statistics 2005).
Ashley, C.A., Banister, C.: Cycling to work from wards in a metropolitan area. 1 Factors influencing cycling to work. Traff. Eng. Con. 30(6), 297–302 (1989)
Baltes, M.: Factors Influencing Nondiscretionary Work Trips by Bicycle Determined from 1990 US Census Metropolitan Statistical Area Data, pp. 96–101. Transportation research record 1538 TRB, National research council, Washington, DC (1996)
Bovy, P.H.L., Den Adel, D.N.: Routekeuzegedrag van fietsers: een analyse met de funktionele meetmethode. Delftse Universitaire Pers, Delft (1985)
Bovy, P.H.L., Bradley, M.A.: Route choice analysed with stated preference approaches. Trans. Res. Rec. 1037, 11–20 (1985)
British Standards: 6399 Part II Loading for buildings. Code of practice for wind loads (1997)
Cervero, R., Radisch, C.: Travel choices in pedestrian versus automobile oriented neighbourhoods. Trans. Pol. 3(3), 127–141 (1996)
CROW: Sign up for the Bike—Design Manual for a Cycle Friendly Infrastructure. Centre for research and contract standardisation in civil engineering, The Netherlands (1993)
Defra: Department of environment food and rural affairs, Countryside Information System http://www.cis-web.org.uk/home/ (Cited 2003)
DfT: National Cycling Strategy. Department for Transport, London www.dft.gov.uk (1996)
DfT: The future of transport—a network for 2030. Transport White Paper, July 2004. Department for Transport, London. www.dft.gov.uk (2004)
Dill, J., Carr, T.: Bicycle commuting and facilities in major US cities. Trans. Res. Rec. 1828 (2003)
Guthrie, N., Davies, D.G., Gardner, G.: Cyclists’ Assessments of Road and Traffic Conditions: The Development of a Cyclability Index. TRL Report 490. Transport Research Laboratory, Crowthorne (2001)
Hopkinson, P., Wardman, M.: Evaluating the demand for new cycle facilities. Trans. Pol. 3(4), 241–249 (1996)
Landis, B.W., Vattikuti, V.R., Brannick, M.T.: Real-time human perceptions toward a bicycle level of service. Trans. Res. Rec. 1578 (1997)
LRC: The Travel Behaviour of Different Ethnic Groups in London. London Research Centre, London (1997)
Meteorological Office: UK climate and weather statistics http://www.met-office.gov.uk/climate/uk/ (cited 2004)
McClintock, H., Cleary, J.: Cycle facilities and cyclists’ safety. Trans. Pol. 3(1/2), 67–77 (1996)
Moudon, A.V., Lee, C., Cheadle, A.D., Collier, C.W., Johnson, D., Schmid, T.L., Weather, R.D.: Cycling and the built environment, a US perspective. Trans. Res. D 11, 245–261 (2005)
National Statistics: Transport Statistics Bulletin, National Travel Survey: 2004 SB (05) 30. Department for Transport, London (2005)
Nelson, A., Allen, D.: If You Build them, Commuters Will Use them: Association between Bicycle Facilities and Bicycle Commuting, pp. 79–83. Transportation research record 1578, TRB, National Research Council, Washington DC (1997)
Ortúzar, J. de D., Iacobelli, A., Valeze, C.: Estimating demand for a cycleway network. Trans. Res. A 34, 353–373 (2000)
Parkin, J.: Comparisons of cycle use for the journey to work from the ‘81, ‘91 and 2001 censuses. Traff. Eng. Con. 44(8), 299–302 (2003)
Parkin, J., Wardman, M., Page, M.: Models of perceived cycling risk and route acceptability. Accident Analysis and Prevention, vol. 39, pp. 364–371. (2007). doi:10.1016/j.aap.2006.08.007
Pindyck, R.S., Rubinfeld, D.L.: Econometric Models and Economic Forecasts, 3rd edn. McGraw-Hill, New York (1991)
Plaut, P.O.: Non-motorized commuting in the US. Trans. Res. D 10, 347–356 (2005)
Rietveld, P., Daniel, V.: Determinants of bicycle use: do municipal policies matter? Trans. Res. A 38, 531–550 (2004)
Stinson, M.A., Bhat, C.R.: Frequency of bicycle commuting: internet-based survey analysis. Transportation Research Board No. 04-3493 (2004)
Stinson, M.A., Bhat C.R.: A comparison of the route preferences of experienced and inexperienced bicycle commuters. Transportation Research Board Paper No. 05-1434 (2005)
Tilahun, N.Y., Levinson, D.M., Krizek, K.J.: Trails, lanes, or traffic: valuing bicycle facilities with an adaptive stated preference survey. Trans. Res. A (2006)
Waldman, J.A.: Cycling in Towns: A Quantitative Investigation. LTR1 Working paper 3. Department for Transport, London (1977) (Out of print)
Wardman, M., Hatfield, R., Page, M.: The UK national cycling strategy: can improved facilities meet the targets? Trans. Pol. 4(2), 123–133 (1997)
Wardman, M., Tight, M., Page, M.: Factors influencing the propensity to cycle to work. Trans. Res. A 41, 339–350 (2007)
Webtag. Guidance on the Appraisal of Walking and Cycling Schemes TAG Unit 3.14.1. Consultation, March. www.webtag.org.uk (2007)
About this article
Cite this article
Parkin, J., Wardman, M. & Page, M. Estimation of the determinants of bicycle mode share for the journey to work using census data . Transportation 35, 93–109 (2008). https://doi.org/10.1007/s11116-007-9137-5
- Journey to work
- Logistic regression model
- Travel demand modelling