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Do preferences for BRT and LRT change as a voter, citizen, tax payer, or self-interested resident?

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Interest in modal preferences remains a topic of high interest as governments make infrastructure decisions that often favour one mode over the other. An informative input into the infrastructure selection process should be the preferences of residents, since they can guide buy into support political and bureaucratic choice making. Cost–benefit analysis (CBA) uses the self-interest preferences of individuals as the relevant interpretation of ‘individual preferences count’, which in aggregate represent the benefit to society of candidate investments. However, the CBA benefit calculations can be rather restrictive with other preference metrics often being identified and used in various ways to inform the debate on infrastructure support. In this paper we assess how the preferences for bus rapid transit (BRT) and light rail transit (LRT) change with different roles the residents may play: a citizen or altruistic resident, a self-interested resident, a tax-payer, and as a voter. We use data collected in five countries to investigate preference differences and also to establish whether there is replicability of the findings across geographical jurisdictions. The findings suggest that there are, in general, noticeable differences in preference revelation across the metrics; however there are also both similarities and differences in the role of specific attribute drivers (as represented by willingness to pay, and magnitude of support for a specific mode) within and between preference metrics across countries.

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  1. Full details of the Ngene syntax, and efficiency outputs for this application, is given in Hensher, Rose, and Greene (2015, Chapter 6.6.3 Design 3: D-Efficient Choice Design).

  2. A within-subject design was used to obtain as much information as possible from each respondent as data were collected in 18 different cities.

  3. 65% of the sample use public transport. 30% have BRT and LRT available. And 58% of the people that have BRT and LRT available, use public transport.

  4. All the model were estimated using PythonBiogeme (Bierlaire 2016).

  5. As described in the ‘Choice Experiment’ section, questions on attribute non-attendance (ANA) were asked at the end of the experiment, where respondents indicated which attributes they did not consider (i.e., those they did not attend to). For more information on ANA refer to Hensher et al. (2015c).

  6. It is also different for the investment than for the system attributes, but this is not relevant for the WTP.

  7. As the models are non-linear in terms of experience and construction cost, individual WTP estimates are highly dependent on the cost attribute levels and individual experience. Therefore, the results presented are equivalent to a WTP that is calculated using the average of the cost attribute level and of the experience levels.


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This paper contributes to the research program of the Volvo Research and Education Foundation Bus Rapid Transit Centre of Excellence (BRT +). We acknowledge the Foundation for funding support. The authors acknowledge the facilities, and the scientific and technical assistance of the Sydney Informatics Hub at the University of Sydney and, in particular, access to the high performance computing facility Artemis. The support of Theo Yeche and Patricia Aranda in translating the survey instrument into French is greatly appreciated. We also thank Rosario Macario (IST, Portugal), Anson Stewart and Chris Zegras (Transportation and Urban Planning, MIT) for their contributions in facilitating access to survey participants. The comments of two referees have materially improved the paper.

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Authors and Affiliations



C. Balbontin: Model estimation, interpretation and writing, D. A. Hensher: Mock up of choice experiment and overall survey instrument, model interpretation and writing, C. Ho: Design of choice experiment and input to overall survey design, C. Mulley: Influence on survey design, review of paper and editing

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Correspondence to Camila Balbontin.

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Appendix A

See Tables 10, 11.

Table 10 Parameter estimates results for the MNL models
Table 11 Parameter estimates results for the HMNL0 models

Appendix B

See Tables 12,13,14,15,16.

Table 12 Willingness to pay estimates ‘Prefer’ model
Table 13 Willingness to pay estimates ‘Metro’ model
Table 14 Willingness to pay estimates ‘Value’ model
Table 15 Willingness to pay estimates ‘Vote’ model
Table 16 Willingness to pay estimates ‘Live’ model

Appendix C

See Fig. 4.

Fig. 4
figure 4

Graphical mean WTP comparison between countries and preference metrics

Appendix D

See Table 17.

Table 17 Example of t-test comparing WTP estimates between models for BRT in Australia

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Balbontin, C., Hensher, D.A., Ho, C. et al. Do preferences for BRT and LRT change as a voter, citizen, tax payer, or self-interested resident?. Transportation 47, 2981–3030 (2020).

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