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

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Camila Balbontin is a Postgraduate Research Fellow at the Institute of Transport and Logistics Studies (ITLS) of University of Sydney. In February 2018, she completed her PhD under the supervision of Professor David Hensher where she focused on integrating decision heuristics and behavioural refinements into travel choice models. She was awarded the ITLS prize for Research Excellence in Transport or Logistics 2017. Camila also holds a bachelor degree in the field of Civil Engineering with a diploma in Industrial Engineering and in Transportation and Logistics from Pontificia Universidad Católica de Chile. She did her MSc degree at the same university under the supervision of Professor Juan de Dios Ortúzar. Her MSc thesis estimated the valuation of households and neighbourhood attributes in the centre of Santiago.
As a Postgraduate Research Fellow, her main focus is choice modelling and travel behaviour. She is currently working on projects related to the BRT Centre of Excellence, business location decisions, hybrid modelling, value uplift, among others.

Working Paper - http://sydney.edu.au/business/itls/research/publications/working_papers

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

  1. 1. The University of Sydney Page 1 Do preferences for BRT and LRT change as a voter, citizen, tax payer, or self- interested resident? Camila Balbontin David A. Hensher Chihn Q. Ho Corinne Mulley June, 2018 BRT Centre of Excellence webinar
  2. 2. The University of Sydney Page 2 ACKNOWLEDGEMENTS 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 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.
  3. 3. The University of Sydney Page 3 OVERVIEW I. Introduction II. Choice Experiment III. Samples IV. Model Formulation V. Results VI. Willingness to Pay VII.Simulated Scenarios VIII.Key Findings
  4. 4. The University of Sydney Page 4 INTRODUCTION – Bus rapid transit (BRT) has often been passed over in favour of light rail transit (LRT) in many geographical settings in developed economies despite having much appeal in delivering high quality services in a cost effective manner. – Well known emotional attachment to rail solutions and image perception Australia France Portugal U.K. U.S.A.
  5. 5. The University of Sydney Page 5 INTRODUCTION The focus of this paper is on whether there are significant differences or similarities in the key behavioural outputs associated with five measures of preference revelation when comparing a BRT system with an LRT system in five countries. Which one would benefit your metropolitan area better? Which one do you personally prefer? Which investment is better value for tax payers’ money? If you were voting now, which one would you vote for? Which investment would improve the liveability of the metropolitan area more?
  6. 6. The University of Sydney Page 6 CHOICE EXPERIMENT – 19 cities in 5 countries. – Design with a BRT and a LRT alternative, both with fixed route length. – Each respondent answers two choice tasks. – At the end, individuals were asked for their: – Socio-demographics and their experience on public transport (number of times used in the last month). – Which attributes they had attended and not attended to (Stated Attribute- Country City Sample Percentage Australia Sydney 271 7% Melbourne 241 6% Canberra 100 3% Brisbane 201 5% All other capital cities 205 6% US Boston 181 5% Los Angeles-Long Beach 300 8% Seattle-Bellevue- Everett 100 3% Minneapolis – St Paul 100 3% Dallas – Fort Worth 150 4% Philadelphia 170 5% Portugal Lisbon 425 14% U.K. Birmingham 274 12% Newcastle 153 6% France Lyon 128 8% Toulouse 137 6% TOTAL 3136
  7. 7. The University of Sydney Page 7 – Illustrative choice screen of the survey
  8. 8. The University of Sydney Page 8 SAMPLES Location Australia U.S. Portugal France U.K. Socioeconomic Profile Age mean (std dev) 44.32 (14.9) 44.42 (16.11) 40.75 (11.45) 42.83 (14.41) 47.52 (15.35) Gender female (%) 54% 61% 52% 53% 50% Personal income in 1000 AUD$ mean (std dev) $61.24 (43.50) $80.57 (60.79) $30.36 (26.11) $50.63 (35.26) $39.12 (31.53) Trip profile % of people that used public transport in the last month 66% 49% 74% 69% 75% Number of times using Bus/BRT in the last month 5.85 (9.13) 7.46 (9.63) 11.46 (14.26) 7.13 (10.19) 7.27 (9.45) Number of times using Light Rail/Tram in the last month 1.73 (4.35) 2.14 (4.33) 2.48 (5.69) 3.91 (6.23) 0.53 (1.76) Number of times using Train/Metro in the last month 6.49 (9.92) 4.29 (7.89) 13.94 (13.87) 9.27 (11.58) 3.55 (6.57)
  9. 9. The University of Sydney Page 9 SAMPLES How many respondents change their response across the five measures of preference revelation? Australia 29% France 32% Portugal 27% U.K. 33% U.S.A. 33%
  10. 10. The University of Sydney Page 10 PREFERENCE MODEL ESTIMATION Hypotheses: – There are significant differences in the drivers and WTP estimates when considering different perspectives – Respondents’ actual experience has a significant influence over preferences – The influence of actual experience is different in the investment characteristics, system characteristics and in the cost attributes Compare the results of the models considering different perspectives Compare models with and without actual experience Compare a model that considers a common influence of experience with a model considering specific influences overt subsets of attributes
  11. 11. The University of Sydney Page 11 PREFERENCE MODEL ESTIMATION – Actual experience is included as conditioning the utility function: – The frequency of use per mode in the last month – As a dummy variable indicating that the respondent used public transport in the last month and has BRT and LRT available in their city To test our hypotheses, we estimate three models: 1) MNL, simple multinomial logit model 2) HMNL0, Heteroscedastic MNL (HMNL), conditioned by actual experience 3) HMNL conditioned by actual experience specific to subset of the attributes
  12. 12. The University of Sydney Page 12 PREFERENCE MODEL ESTIMATION 1 1 1 1 1, 1, 1 , , , , inv sys scst bus metro train MNL BRT BRT x inv BRT x sys BRT Z x cst BRT fr BRT bus freq BRT metro freq BRT train U ASC x x Z x freq freq freq                       1 1 11, 1, , _ _ inv sys scst MNL LRT LRT x inv LRT x sys LRT x cst LRT usePT BRTLRT usePT BRTLRT U ASC x x x dummy              Simple MNL Investment characteristi cs System characteristi cs Cost characteristi cs Socio- demographics Frequency of bus, metro and train use in the last month Dummy that is one if the respondent used public transport in the last month and he/she has BRT and LRT available in their city
  13. 13. The University of Sydney Page 13 PREFERENCE MODEL ESTIMATION HMNL0 1 1 1 1 0 , , , 1, 1, 1 , (1 ) ... ... ... bus metro train inv sys scst HMNL BRT fr BRT bus freq BRT metro freq BRT train BRT x inv BRT x sys BRT Z x cst BRT U freq freq freq ASC x x Z x                             1 1 1 0 _ _ 1, 1, , (1 ) ... ... bus metro train inv sys scst HMNL LRT freq bus freq metro freq train usePT BRTLRT usePT BRTLRT LRT x inv LRT x sys LRT x cst LRT U freq freq freq dummy ASC x x x                            Frequencies of modes use condition the utility function Same with the dummy variable representing PT use with BRT and LRT availability Utility functions are equivalent in terms of investment, system and cost characteristics, as well as socio- demographics
  14. 14. The University of Sydney Page 14 PREFERENCE MODEL ESTIMATION HMNL 1 , , , 1, , , , (1 ) ... (1 bus metro train inv bus metro train HMNL BRT fr BRTinv bus freq BRTinv metro freq BRTinv train BRT x inv BRT fr BRTsys bus freq BRTsys metro freq BRTsys tr U freq freq freq ASC x freq freq freq                            1 1 1 1, 1 , , , , ) ... ... (1 ) sys bus metro train scst ain x sys BRT Z fr BRTcst bus freq BRTcst metro freq BRTcst train x cst BRT x Z freq freq freq x                           1 , , , _ , _ 1, (1 ) ... ... bus metro train inv HMNL LRT freq inv bus freq inv metro freq inv train usePT BRTLRT inv usePT BRTLRT LRT x inv LRT U freq freq freq dummy ASC x                      Overt experience has a difference influence on the investment, system and cost characteristics Investment characteristics System characteristics Cost characteristics Equivalent for LRT adding the experience dummy variable PT use with availability for each subset of attributes
  15. 15. The University of Sydney Page 15 COMPARISON OF THE MODELS – VUONG TEST Vuong Test Prefer Metro Value Vote Live HMNL vs MNL Vuong Statistic 1.825 0.508 1.698 1.283 2.257 Result Favours HMNL with 90% confidence level No statistically significant improvement Favours HMNL with 90% confidence level Favours HMNL with 80% confidence level Favours HMNL with 95% confidence level HMNL vs HMNL0 Vuong Statistic 1.236 0.154 2.081 0.905 2.690 Result Favours HMNL with 75% confidence level No statistically significant improvement Favours HMNL with 95% confidence level No statistically significant improvement Favours HMNL with 99% confidence level Preferred Model  HMNL
  16. 16. The University of Sydney Page 16 EMPIRICAL FINDINGS PREFERRED HMNL MODEL – Gender was the only socio-demographic characteristic which was significant  in the personal preference model (‘Prefer’) and the model associated with benefits in the metropolitan area (‘Metro’). – Female respondents were more inclined towards the BRT alternative (often associated with safety in the presence of a driver).
  17. 17. The University of Sydney Page 17 EMPIRICAL FINDINGS - DRIVERS BRT LRT Prefer Metro Value Vote Live Prefer Metro Value Vote Live Investment Characteristics Construction cost ($m) X X X X X X X X X X Construction time (year) X X - X X X (A, UK) X X X - Percent metro population serviced (%) X X X X X X X - X X Percent right of way - X - X X X - X - X Annual operating and maintenance cost ($m) X - X X X - X - - - Operation period assured (year) X - X X X - X - - - Risk of being closed after assured period (%) - X X X X X (P) X (P) - X (P) - Environmental friendliness (% better/worse vs. car) X (P) - - X (P) X (P) X X X X X Percent car switched to this mode (%) X (F) X X (F) X (F) X (F) - - X - - High level of business attracted to station/stop (1/0) - - - - - X X X X X System Characteristics One-way service capacity ('1000 passengers) X X (A) - X (A) X (A) - - - - - Off-peak headway (mins) X (A) X X - X (A) - - - - - Travel time compared to car (% quicker/slower) X X - X X X X - X X Travel cost compared to car (% cheaper/dearer) X X X X X X X X X X Off-vehicle prepaid ticket required (1/0) X - - X (F) X (F) X - - - X Integrated fare availability (1/0) X - X (A) - X (A) - X (A) - X - Waiting time if transfer (mins) - - - - - X X X (US, P) X X Staff presence on board (1/0) - - - - - X X X X X Level boarding (vs. step boarding) - - - X - - - - - - Total number of investment and system drivers 13 10 9 14 15 12 13 9 11 10
  18. 18. The University of Sydney Page 18 EMPIRICAL FINDINGS - EXPERIENCE Experience Prefer Metro Value Vote Live Frequency bus conditioning investment characteristics X X - X X Frequency rail conditioning investment characteristics X - - - - Frequency metro conditioning investment characteristics X X X - X Frequency bus conditioning system characteristics - X X - X Frequency rail conditioning system characteristics X - - - - Frequency metro conditioning system characteristics X X - X X Frequency bus conditioning cost X - X - - Dummy UsedPT_BRTLRT conditioning investment characteristics X - - - X Dummy UsedPT_BRTLRT conditioning cost X - - - - Total number of significant experience parameters 8 4 3 2 5
  19. 19. The University of Sydney Page 19 WILLINGNESS TO PAY ESTIMATES Reduce construction time by 1 year Increase by 1% the population served Increase by 1% the right of way 0.00 2.00 4.00 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD $m) Reduce the construction time in one year BRT LRT 0.00 1.00 2.00 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD $m) Increase the population serviced by 1% BRT LRT 0.00 0.10 0.20 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD $m) BRT LRT Increase the percentage right of way by 1% 0.00 0.10 0.20 0.30 0.40 0.50 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD$) Prefer Metro Value Vote Live BRT LRT Reduce the travel cost compared to the car by 1%
  20. 20. The University of Sydney Page 20 WILLINGNESS TO PAY ESTIMATES Increase by 1% the environmental friendliness compared to car Reduce travel time relative to car by 1% Reduce travel cost relative to car by 1% 0.00 1.00 2.00 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD $m) BRT LRT Increase the environmental friendliness in 1% compared to the car 0.00 0.20 0.40 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD $m) BRT LRT Increase the travel time compared to the car by 1% quicker 0.00 0.20 0.40 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD $m) BRT LRT Reduce the travel cost compared to the car by 1% 0.00 0.10 0.20 0.30 0.40 0.50 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD$) Prefer Metro Value Vote Live BRT LRT Reduce the travel cost compared to the car by 1%
  21. 21. The University of Sydney Page 21 SIMULATED SCENARIOS Level of support towards BRT in base scenarios for each country: Prefer 47.52% 47.44% 47.82% 49.57% 43.76% Metro 46.97% 46.42% 49.03% 47.29% 47.21% Value 49.24% 51.57% 49.28% 46.70% 48.90% Vote 44.96% 46.17% 44.78% 46.12% 48.90% Live 44.96% 46.17% 44.78% 46.12% 47.51% Australia France Portugal U.K. U.S.A.
  22. 22. The University of Sydney Page 22 SIMULATED SCENARIOS – CONSTRUCTION COSTS 0% 2% 4% 6% 8% %ofsupportchange LRT construction cost is double that of BRT -15% -10% -5% 0% %ofsupportchange BRT construction cost is double that of LRT 0.00 0.10 0.20 0.30 0.40 0.50 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD$) Prefer Metro Value Vote Live BRT LRT Reduce the travel cost compared to the car by 1%
  23. 23. The University of Sydney Page 23 SIMULATED SCENARIOS – CONSTRUCTION TIMES0.00 0.10 0.20 0.30 0.40 0.50 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD$) Prefer Metro Value Vote Live BRT LRT Reduce the travel cost compared to the car by 1% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% %ofsupportchange LRT construction time is double that of BRT -2.5% -2.0% -1.5% -1.0% -0.5% 0.0% %ofsupportchange BRT construction time is double that of LRT
  24. 24. The University of Sydney Page 24 SIMULATED SCENARIOS – CATCHMENT AREA0.00 0.10 0.20 0.30 0.40 0.50 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD$) Prefer Metro Value Vote Live BRT LRT Reduce the travel cost compared to the car by 1% 0.0% 0.5% 1.0% 1.5% 2.0% %ofsupportchange BRT serves 50% more people -2.5% -2.0% -1.5% -1.0% -0.5% 0.0% %ofsupportchange LRT serves 50% more people
  25. 25. The University of Sydney Page 25 SIMULATED SCENARIOS – SUSTAINABILITY0.00 0.10 0.20 0.30 0.40 0.50 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD$) Prefer Metro Value Vote Live BRT LRT Reduce the travel cost compared to the car by 1% -5.0% -4.0% -3.0% -2.0% -1.0% 0.0% %ofsupportchange LRT is 10% more environmentally friendly
  26. 26. The University of Sydney Page 26 SIMULATED SCENARIOS – EXPERIENCE -4.0% -2.0% 0.0% 2.0% 4.0% 6.0% %ofsupportchange Bus frequency of use increases 100% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0% %ofsupportchange Metro frequency of use increases 100% 0.00 0.10 0.20 0.30 0.40 0.50 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD$) Prefer Metro Value Vote Live BRT LRT Reduce the travel cost compared to the car by 1%
  27. 27. The University of Sydney Page 27 SIMULATED SCENARIOS – AVAILABILITY0.00 0.10 0.20 0.30 0.40 0.50 0.60 Australia France Portugal UK US Australia France Portugal UK US WTP (AUD$) Prefer Metro Value Vote Live BRT LRT Reduce the travel cost compared to the car by 1% -3.0% -2.0% -1.0% 0.0% 1.0% 2.0% Australia U.S. %ofsupportchange All the cities have BRT and LRT available
  28. 28. The University of Sydney Page 28 KEY FINDINGS – Experience seems to have a significant influence regarding overall performance of the model, and the influence seems to vary across subsets of attributes. – However, the influence of experience on preferences for BRT or LRT is small. This is an important finding since it takes the pressure off the much promoted position that until you experience a mode you are unlikely to obtain sufficient community support for it.
  29. 29. The University of Sydney Page 29 KEY FINDINGS – Different measures of preference revelation produce noticeable differences in the levels of WTP, including different subsets of statistically significant WTP estimates. – There is clear evidence of preference heterogeneity between countries between modal support drivers and between modes, suggesting that replication as a basis of transferability of evidence is potentially problematic.
  30. 30. The University of Sydney Page 30 KEY FINDINGS – The scenario simulations show some high sensitivities in the levels of support for one mode over the other. – The greatest percent changes are associated with construction cost, construction time, and environmental friendliness.
  31. 31. The University of Sydney Page 31 KEY FINDINGS – Historically, cost benefit analysis has used self- interest or private consumer preference, and to avoid any risk of double counting, this should remain the appropriate metric to capture societal preferences in CBA. – The formal economic cost-benefit analysis can be complemented by incorporating the preferences of residents as expressed in a number of other ways. – The results shows that the voice of residents has a number of interpretations that might highly influence the outcomes.
  32. 32. The University of Sydney Page 32 KEY FINDINGS – This has the intent, amongst other reasons, of drawing to the attention of politicians, their advisers and the government bureaucracy, that the voice of the residents has a number of interpretations that have buy in appeal and will likely lead to a positive electoral outcome, regardless of the CBA finding.
  33. 33. The University of Sydney Page 33 Do preferences for BRT and LRT change as a voter, citizen, tax payer, or self- interested resident? Camila Balbontin David A. Hensher Chihn Q. Ho Corinne Mulley June, 2018 BRT Centre of Excellence webinar

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