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Modal image: candidate drivers of preference differences for BRT and LRT

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Abstract

The physical image of transport systems, as perceived by users and non users, has long been put forward as a powerful influence on the formation of preferences. One setting for this is in the choice between bus rapid transit (BRT) and light rail transit (LRT) where there appears to be a strong preference in favour of LRT in developed countries and the reverse in developing countries. Using data collected in six capital cities in Australia in 2013, in which individuals rated two BRT and two LRT designs presented as physical images, we develop a full rank mixed logit model to identify candidate sources of influence on image preferences. These provide signals to assist in preparing the ground for a segmented profile for policy makers and politicians to understand how to underpin building a rational debate for modal options in our cities.

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Notes

  1. Ties can easily be accommodated.

  2. We investigated latent class models, scaled MNL, and exploded random parameter logit, but none of these model forms were a statistical improvement over MMNL.

  3. The other data available was whether full time, part time employed, student, retired, hours worked per week, gender, number of adults and number of children in household.

  4. The Cholesky decomposition matrix is a lower triangular matrix (meaning the upper off-diagonal elements of the matrix are all zero). When we have more than one random parameter and we permit correlated random parameters then the standard deviations are no longer independent. To assess this we have to decompose the standard deviation parameters into their attribute-specific and attribute-interaction standard deviations. Cholesky decomposition is the method used to do this. The mixed logit model is extended to accommodate this case by allowing the set of random parameters to have an unrestricted covariance matrix. The nonzero off-diagonal element of this matrix carries the cross-parameter correlations.

  5. In various seminars, Hensher has suggested a name change to ‘Dedicated Corridor Rapid Transit’ (DCRT) in order to emphasise the key features of the modal service, avoiding the use of the word ‘bus’.

References

  • Barlach, Y., Shiftan, Y., Sheffer, D.: Passengers Attitude Toward Bus And Rail: Investigation of Corridor with a Similar Level of Service. In 11th World Conference on Transport Research, Istanbul (2007)

  • Beirao, G., Cabral, J.A.S.: Understanding attitudes towards public transport and private car: a qualitative study. Transp. Policy 14, 478–489 (2007)

    Article  Google Scholar 

  • Cirillo, C., Eboli, L., Mazzulla, G.: On the asymmetric user perception of transit service quality. Int. J. Sustain. Transp. 5(4), 216–232 (2011)

    Article  Google Scholar 

  • dell’Olio, L., Ibeas, A., Cecìn, P.: Modelling user perception of bus transit quality. Transp. Policy 17, 388–397 (2010)

    Article  Google Scholar 

  • dell’Olio, L., Ibeas, A., Cecìn, P.: The quality of service desired by public transport users. Transp. Policy 18(1), 217–227 (2011)

    Article  Google Scholar 

  • Eboli, L., Mazzulla, G.: An SP experiment for measuring service quality in public transport. Transp. Plan. Technol. 31(5), 509–523 (2008a)

    Article  Google Scholar 

  • Eboli, L., Mazzulla, G.: Willingness-to-pay of public transport users for improvement in service quality. Eur. Transp. 38, 107–118 (2008b)

    Google Scholar 

  • Eboli, L., Mazzulla, G.: How to capture the passengers’ point of view on a transit service through rating and choice options. Transp. Rev. 30(4), 435–450 (2010)

    Article  Google Scholar 

  • Ellaway, A., Macintyre, S., Hiscock, R., Kearns, A.: In the driving seat: psychosocial benefits from private motor vehicle transport compared to public transport. Transp. Res. Part F 6, 217–321 (2003)

    Article  Google Scholar 

  • Federal Transit Administration (FTA). Quantifying the importance of image and perception to bus rapid transit. United States Department of Transportation and FTA Office of Research, Project No: FTA-FL-26-7109.2009.3 (2009)

  • Hankinson, G.: Location branding: a study of the branding practices of 12 English cities. J Brand Manag 9(2), 127–142 (2001)

    Article  Google Scholar 

  • Hankinson, G.: The brand images of tourism destinations: a study of the saliency of organic images. J. Prod. Brand Manag. 13(1), 6–14 (2004)

    Article  Google Scholar 

  • Hatton MacDonald, D., Morrison, M., Rose, J. M. Boyle, K.: Untangling differences in values from internet and mail stated preference studies, Fourth World Congress of Environmental and Resource Economists, Montreal Canada, June 28–July 2 (2010)

  • Hensher, D.A.: Sustainable public transport systems: moving towards a value for money and network-based approach and away from blind commitment. Transp. Policy 14(1), 98–102 (2007)

    Article  Google Scholar 

  • Hensher, D.A.: The relationship between bus contract costs, user perceived service quality and performance assessment. Int. J. Sustain. Transp. 8(1), 5–27 (2014). doi:10.1080/15568318.2012.758454. special issue

    Article  Google Scholar 

  • Hensher, D.A., Waters II, W.G.: Light rail and bus priority systems: choice or blind commitment? In: Starr Macmullen, B. (ed.) Research in transportation economics, vol. 3, pp. 139–162. JAI Press, Greenwich (1994)

    Google Scholar 

  • Hensher, D.A., Rose, J.M., Greene, W.H.: Applied choice analysis: a primer. Cambridge University Press, Cambridge (2005)

    Book  Google Scholar 

  • Hensher, D. A., Mulley, C., Rose, J. M.: Understanding the relationship between voting preferences for public transport and perceptions and preferences for bus rapid transit versus light rail, BRT Centre Perceptions Study Paper #2, accepted for Journal of Transport Economics and Policy, 10 March (2014)

  • Innocenti, A., Lattarulo, P., Pazienza, M.G.: Car stickiness: heuristics and biases in travel choice. Transp. Policy 25, 158–168 (2013)

    Article  Google Scholar 

  • Lin, C.-H., Morais, D.B., Kerstelter, D.L., Hou, J.-S.: Examining the role of cognitive and affective image in predicting choice across natural, developed and theme-park destinations. J. Travel Res. 46, 183–194 (2007)

    Article  Google Scholar 

  • Lindhjem, H., Navrud, S.: Using internet in stated preference surveys: a review and comparison of survey modes. Int. Rev. Environ. Resour. Econ. 5, 309–351 (2011)

    Article  Google Scholar 

  • Marcucci, E., Gatta, V.: Quality and public transport service contracts. Eur. Transp. 36, 92–106 (2007)

    Google Scholar 

  • Merrilees, B., Miller, D., Herington, C.: Antecedents of residents’ city brand attitudes. J. Bus. Res. 62, 326–367 (2009)

    Article  Google Scholar 

  • Ohnmacht, M.S.: Differences in cognition of public transport systems: image and behaviour towards urban public transport. Dissertation submitted to ETH Zurich for the degree of Dr of Sciences (unpublished) (2012)

  • Redman, L., Friman, M., Garling, T., Hartig, T.: Quality attributes of public transport that attract car users: a research review. Transp. Policy 25, 119–127 (2013)

    Article  Google Scholar 

  • Rietveld, P.: Six reasons why supply orientated indicators systematically overestimate service quality in public transport. Transp. Rev. 25(3), 319–328 (2005)

    Article  Google Scholar 

  • Steg, L.: Car use lust and must. Transp. Res. Part A 39(2005), 147–162 (2005)

    Google Scholar 

  • Stradling, S.: Transport user needs and marketing public transport. Proc. Inst. Civil Eng. Munic. Eng. 151(1), 23–28 (2002)

    Google Scholar 

  • TCRP (Transit Cooperative Research Program): TCRP Report 63: enhancing the visibility and image of transit in the United States and Canada, TRB. National Academy Press, Washington DC (2000)

    Google Scholar 

  • Tirachini, A., Hensher, D.A., Rose, J.M.: Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transp. Res. Part A 53, 36–52 (2013)

    Google Scholar 

  • Train, K.E.: Discrete choice methods with simulation, 2nd edn. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  • Tversky, A., Kahneman, D.: Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974)

    Article  Google Scholar 

  • Wright, C., Egan, J.: De-marketing the car. Transp. Policy 7, 287–294 (2000)

    Article  Google Scholar 

  • Virgo, B., de Chernatony, L.: Delphic brand visioning to align stakeholder buy-into the City of Birmingham brand. J. Brand Manag. 13(6), 379–392 (2006)

    Article  Google Scholar 

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Acknowledgments

Research funded under the Australian Research Council Discovery Program grant DP0770618 and the Volvo Research and Education Foundation Bus Rapid Transit Centre of Excellence. We acknowledge the Foundation for funding support and the contribution of John Rose and Jun Zhang of ITLS to the overall study. The comments of two referees and Tom van Vuren have materially improved this paper.

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Authors and Affiliations

  1. Institute of Transport and Logistics Studies, The University of Sydney Business School, University of Sydney, Sydney, NSW, 2006, Australia

    David A. Hensher & Corinne Mulley

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  1. David A. Hensher
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  2. Corinne Mulley
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Correspondence to David A. Hensher.

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Hensher, D.A., Mulley, C. Modal image: candidate drivers of preference differences for BRT and LRT. Transportation 42, 7–23 (2015). https://doi.org/10.1007/s11116-014-9516-7

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