, Volume 41, Issue 6, pp 1287–1304 | Cite as

Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: a hybrid choice modelling approach

  • Rafael Maldonado-HinarejosEmail author
  • Aruna Sivakumar
  • John W. Polak


Cycling is often promoted as a means of reducing urban congestion and improving health, social and environmental outcomes. However, the quantification of these potential benefits is not well established. This is due in part to practical difficulties in estimating cycling demand and a lack of sound methodologies to appraise cycling initiatives. In this paper we attempt to address this need by developing predictive models of cycle demand, relative to other transport modes, that capture not only the impacts of observed characteristics such as age and travel time but also the role of attitudes and perceptions. Using data from a stated preference survey, we estimate a hybrid choice model for cycle use that incorporates the role of attitudes towards cycling, perceptions of the image associated with cycling, and the stress arising from safety concerns. Model results indicate that the latent attitudes and perceptions explain an important part of the non-observable utility in a simple multinomial logit choice model. We also demonstrate policy analysis using the hybrid choice model, which allows comparisons of ‘hard’ policies such as the provision of parking facilities against ‘soft’ measures such as cycle promotion schemes.


Attitudes and perceptions Cycling demand Transport policy Discrete choice models Predictive models Stated preference data 



The authors are grateful to Clare Sheffield at Transport for London and Tony Duckenfield at Steer Davies Gleave in making available to us the data used in this study. However, all the analyses and conclusions reported here are the responsibility of the authors alone, and do not necessarily represent the views of Transport for London or Steer Davies Gleave.


  1. Akar, G., Clifton, K.J.: The influence of individual perceptions and bicycle infrastructure on the decision to bike. Transp. Res. Rec. (2009)Google Scholar
  2. An, M., Chen, M.:Estimating non-motorized travel demand. Transp. Res. Rec. (2007)Google Scholar
  3. Barnes, G., Krizek, K.: Estimating bicycling demand. Transp. Res. Rec. 1939, 45–51 (2005)CrossRefGoogle Scholar
  4. Ben-Akiva, M.E., Walker, J.L., Bernardino, A.T., Gopinath, D.A., Morikawa, T., Polydoropoulou, A.: Integration of choice and latent variable models. In: Mahmassani, H.S. (ed.) Perpetual Motion: Travel Behaviour Research Opportunities and Challenges. Pergamon, Amsterdam (2002)Google Scholar
  5. Bolduc, D., Boucher, N., Alvarez-Daziano, R.: Hybrid choice modelling of new technologies for car choice in Canada. Transp. Res. Rec. 2082, 63–71 (2008)CrossRefGoogle Scholar
  6. Bollen, K.A.: Latent variables in psychology and the social sciences. Ann. Rev. Psychol. 53, 605–634 (2002)CrossRefGoogle Scholar
  7. Börjesson, M., Eliasson, J.: The value of time and external benefits in bicycle cost-benefit analyses. Centre for Transport Studies, Royal Institute of Technology, Sweden (2010)Google Scholar
  8. Cao, X., Mokhtarian, P.L., Handy, S.L.: Examining the impacts of residential self-6 selection on travel behavior: a focus on empirical findings. Transp. Rev. 29(3), 359–395 (2009)CrossRefGoogle Scholar
  9. Cervero, R., Duncan, M.: Walking, bicycling, and urban landscapes: evidence from the San Francisco Bay area. Am. J. Public Health, September 2003 93(9) (2003)Google Scholar
  10. Chorus, C.G., Kroesen, M.: On the (im-)possibility of deriving transport policy implications from hybrid choice models. Working paper, transport and logistics group, Delft University of Technology (2014)Google Scholar
  11. Daly, A.J., Hess, S., Patruni, B., Potoglou, D., Rohr, C.: Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour. Transportation 39(2), 267–297 (2012)CrossRefGoogle Scholar
  12. de Ortúzar, J.D., Willumsen, L.G.: Modelling transport, vol. 3. John Wiley and Sons, Chichester (2001)Google Scholar
  13. Dill, J., Voros, K.: Factors affecting Bicycling Demand: initial survey findings from the Portland Oregon Region. Transp. Res. Rec. 2031, 9–17 (2007)CrossRefGoogle Scholar
  14. Forsyth, A. and Krizek, K.: Walking and bicycling: what works for planners? Built Environ. (2010)Google Scholar
  15. Fujii, S., Gärling, T.: Application of attitude theory for improved predictive accuracy of stated preference methods in travel demand analysis. Transp. Res. 37A, 389–402 (2003)Google Scholar
  16. Gärling, T.: Behavioral assumptions overlooked in travel-choice modelling. In: Ortuzar, J., Jara-Diaz, S., Hensher, D. (eds.) Transport modelling, pp. 3–18. Pergamon, Oxford (2008)Google Scholar
  17. Gatersleben, B., Appleton, K.M.: Contemplating cycling to work: attitudes and perceptions in different stages of change. Transp. Res. Part A: Policy Pract. 41(4), 302–312 (2007)Google Scholar
  18. Golob, T.F.: Joint models of attitudes and behavior in evaluation of the San Diego I-15 congestion pricing project. Transp. Res. Part A 35, 495–514 (2001)Google Scholar
  19. Habib K.M.N., Zaman H.: Effects of incorporating latent and attitudinal information in mode choice model. 45th Transportation and logistics trends and policies: successes and failures (2010)Google Scholar
  20. Hall, P.: Theoretical comparison of bootstrap confidence intervals. Ann. Statisi. 16, 1–50 (1988)CrossRefGoogle Scholar
  21. Hallett, I., Luskin, D., Machemehl, R.: Evaluation of On-Street Bicycle Facilities Added to Existing Roadways,T. D. O. Transportation. Austin, TX (2006)Google Scholar
  22. Handy, S. L., Heinen, E. and Krizek, K.: Cycling in small cities. In: John Pucher and Ralph Buehler (Eds). Cycling for Sustainable Transport: International Trends and Policies. MIT Press (2012)Google Scholar
  23. Heinena, E., Maat, K., van Wee, B.: The role of attitudes toward characteristics of bicycle commuting on the choice to cycle to work over various distances. Transp. Res. Part D: Transp Environ. 16(2), 102–109 (2011)CrossRefGoogle Scholar
  24. Hochmair, H.H.: Assessment of latent bicycle demand in street networks. Research and Education Center, University of Florida (2009)Google Scholar
  25. Hunt, J.D., Abraham, J.E.: Influences on bicycle use. Transportation 34, 453–470 (2007)CrossRefGoogle Scholar
  26. Kamargianni, M., Polydoropoulou, A.: Hybrid choice model to investigate effects of teenagers’ attitudes toward walking and cycling on mode choice behavior. Travel Behav. 1, 151–161 (2013)Google Scholar
  27. Koppelman, F.S., Pas, E.I.: Travel-choice behaviour: models of perceptions, feelings, preference, and choice. Transp. Res. Rec. 765, 26–33 (1980)Google Scholar
  28. Krizek, K., Forsyth, A., Baum, L.: Walking and cycling international literature review. Melbourne: Victoria Department of Transport (2009)Google Scholar
  29. Kuppam, A.R., Pendyala, R.M., Rahman, S.: Analysis of the role of traveller attitudes and perceptions in explaining mode-choice behaviour. Transp. Res. Rec. 1676, 68–76 (1999)CrossRefGoogle Scholar
  30. Madanat, S.M., Yang, C.Y.D., Yen, Y.M.: Analysis of stated route diversion intentions under advanced traveller information systems using latent variable modelling. Transp. Res. Rec. 1485, 10–17 (1995)Google Scholar
  31. Maldonado-Hinarejos, R.: Modelling the impacts of smarter choices on transport demand. Case study: Promoting cycling in London. Master thesis. Centre for Transport Studies, Imperial College London (2011)Google Scholar
  32. Mokhtarian, P.L., Salomon, I.: Modeling the desire to telecommute: the importance of attitudinal factors in behavioural models. Transp. Res. Part A 31, 35–50 (1997)Google Scholar
  33. Morikawa, T., Ben-Akiva, M., McFadden, D.: Discrete choice models incorporating revealed preferences and psychometric data. Econom. Models Mark. 16, 27–53 (2002)Google Scholar
  34. Niemeier, A.: Longitudinal analysis of bicycle count variability: results and modeling implications. J. Transp. Eng. May–June (1996)Google Scholar
  35. Ortúzar, de J.D., Hutt, G.A.: La influencia de elementos subjetivos en funciones desagregadas de elección discreta. Ingeniería de Sistemas 4, 37–54 (in Spanish) (1984)Google Scholar
  36. 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)CrossRefGoogle Scholar
  37. Porter, C., Suhrbier, J., Schwartz, W.: Forecasting bicycle and pedestrian travel: state of the practice and research needs. Transp. Res. Rec. 1674, 94–101 (1999)CrossRefGoogle Scholar
  38. Prashker, J.N.: Mode choice models with perceived reliability measures. Transp. Eng. J. 105, 251–262 (1979)Google Scholar
  39. Raveau, S., R. Alvarez-Daziano, M.F. Yáñez, D. Bolduc and J. de D. Ortúzar: Sequential and simultaneous estimation of hybrid discrete choice models: some new findings. Transp. Res. Rec. (2010)Google Scholar
  40. Rose, G., Ahmed, F., Figliozzi, M., Jacob, C.: Quantifying and comparing the effects of weather on bicycle demand in Melbourne (Australia) and Portland (USA). Transp. Res. Board. (2011)Google Scholar
  41. Rybarczyka, G., Wu, C.: Bicycle facility planning using GIS and multi-criteria decision analysis. Appl. Geogr. 30, 282–293 (2010)CrossRefGoogle Scholar
  42. Ryley, T.J.: Estimating cycling demand for the journey to work or study in West Edinburgh, Scotland. Transp. Res. Rec. 1982, 187–193 (2006)CrossRefGoogle Scholar
  43. Skrondal, A., Rabe-Hesketh, S.: Latent variable modelling: a survey. Scand. J. Stat. 34, 712–745 (2007)CrossRefGoogle Scholar
  44. Thomas, T., Jaarsma, R., Tutert, B.: Temporal variations of bicycle demand in the Netherlands: the influence of weather on cycling. Transp. Res. Rec. (2009)Google Scholar
  45. Vandenbulcke, G., Dujardin, C., Thomas, I., de Gues, B., Degraeuwe, B., Meeusen, R., Panis, L.I.: Cycle commuting in Belgium: spatial determinants and ‘re-cycling’ strategies. Transp. Res. Part A 45, 118–137 (2011)Google Scholar
  46. Vredin Johansson, M., Heldt, T., Johansson, P.: The effects of attitudes and personality traits on mode choice. Transp. Res. Part A 40, 507–525 (2006)Google Scholar
  47. Walker, J.: Extended discrete choice models: integrated framework, flexible error structures, and latent variables, Ph.D. Dissertation MIT, Massachusetts (2001)Google Scholar
  48. Wardman, M.R., Tight, M.R., Page, M.: Factors influencing the propensity to cycle to work. Transp. Res. A 41(4), 339–359 (2007)Google Scholar
  49. Yáñez, M.F., Mansilla, P., de Ortúzar, J.D.: The santiago panel: measuring the effects of implementing transantiago. Transportation 37, 125–149 (2009)CrossRefGoogle Scholar
  50. Yáñez, M.F., Raveau, S., de Ortúzar, J.D.: Inclusion of latent variables in Mixed Logit models: modelling and forecasting. Transp. Res. Part A 44, 744–753 (2010)Google Scholar
  51. Yi, M., Feeney, K., Adams, A., Garcia, C. and Chandra P.: Valuing cycling—Evaluating the economic benefits of providing dedicated cycle ways at a strategic network level. Australasian Transport Research Forum 2011 Proceedings 28–30 September, Adelaide, Australia (2011)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Rafael Maldonado-Hinarejos
    • 1
    Email author
  • Aruna Sivakumar
    • 1
  • John W. Polak
    • 1
  1. 1.Department of Civil and Environmental Engineering, Imperial College LondonCentre for Transport StudiesLondonUK

Personalised recommendations