2015-01-13 Buy in for BRT over LRT how to inform project planning prioritisation about resident preferences by Corinne Mulley

1. Identifying resident preferences for public
transport investments: a buy-in perspective
David Hensher, Chinh Ho, and Corinne Mulley
Institute of Transport and Logistics Studies
BRT CoE Board Meeting
January 13th, 2015 Washington, D.C.

2. Outline
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› Introduction
› Public preference survey
› Model
› Results
› Summary and policy relevance

3. Introduction
› Urban areas face increasing demand for new public transport investments.
› Choices to voters are often not given in terms of alternative costs – so
fixed length projects will have different costs depending on mode
› Public preference is important to understand from the community
perspective
- How government should spend money and gain voter support, and
- To answer questions like
- How the public would temper their preference for a new modern light rail
system if it cost much more than a BRT of the same route length?
› An international comparison is important to see if features of Australia are
common or different from other jurisdictions
- Can we provide common advice to cities to promote BRT that voters (and
therefore politicians) want?
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4. Drivers of Public Preferences for PT
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› Relevant factors associated with modal image, service quality and voting
preferences were shortlisted through a best–worst experiment (phase I).
› 20 key drivers are classified into four groups:
- Investment (construction time, route length, population coverage, ROW, maintenance cost)
- Service (capacity, peak and off-peak frequency, travel time vs car, fare vs car-related costs)
- Design (Ticketing, Transfer, boarding, safety and security)
- General characteristics (assured period of operation, risk of closing down after this assured
period, level of attracting business around stations/stops, % car users switch to the system,
environmental friendliness).
› The list of drivers is not complete for some respondents while surplus for
others. The survey instrument accounts for this.

5. Choice Experiment – Unlabelled
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6. Choice Experiment – Labelled
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7. Choice Experiment – Attribute Non-Attendance
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8. The Survey
› Online survey with panels from PureProfile and SSI
› A pilot survey design used D-error measure and distributed to 200
respondents (100 samples from each panel)
› Model estimated on the pilot sample to obtain priors for the main survey
› Stated Choice Experiment redesigned and distributed to 400 PureProfile
respondents
› Model re-estimated on 600 samples to obtain more accurate priors
› SC survey redesigned and distributed to 400 SSI respondents
› Respondents were sought in all Australian capital cities with population-
based quotas applied for each phase
› A total sample of 1,018 respondents was obtained (18 surplus), giving
2,036 observations for final model estimation
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9. The Sample
City Actual Target
Sydney 271 270
Melbourne 241 240
Canberra 100 100
Brisbane 201 200
Adelaide 80 50
Perth 70 50
Darwin 21 50
Hobart 34 50
Total 1,018 1,010
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Socio-economic profile Mean (std.dev)
Age (years) 43.84 (15.5)
Proportion full time employed 0.41
Proportion part time employed 0.19
Proportion students 0.17
Working hours per week 20.75 (16.99)
Number of adults in household 2.11 (0.89)
Number of children in household 0.66 (1.03)
Personal income in $1000 62.47 (40.47)
Number of cars in household 1.66 (0.98)
Member of PT association (%) 9
Member of env. association (%) 6

10. Estimation Results using a Random Regret Mixed
Logit model
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Parameter estimates
(t-values)
Means of random parameters:
Modal option (BRT = 1, LRT = 0) -0.2136 (-3.95)
Construction time (years) -0.0525 (-6.75)
Construction cost ($m) -0.1009 (-7.80)
On-board staff presence (yes=1) 0.2642 (5.27)
Risk of being closed after assured period (yes=1) -0.0009 (-1.25)
Overall environmental friendliness compared to car (%) 0.0097 (5.63)
Non-random parameters:
Percent metro population serviced (%) 0.0295 (7.10)
Percent of route dedicated to this system only (%) 0.0037 (4.37)
Annual operating and maintenance cost ($m) -0.0148 (-3.04)
Service capacity in one direction (‘000s passengers/hour) 0.0135 (5.72)
Peak service frequency (every x mins) -0.0176 (-2.48)
Off- peak service frequency (every x mins) -0.0128 (-2.42)
Travel time (door to door) compared to car (% quicker) 0.0128 (6.41)
Travel cost compared to car (%) -0.0063 (-3.97)
Integrated fare availability (yes=1) 0.2922 (5.92)
Boarding (level =1, step = 0) 0.2002 (4.02)
Respondents in Brisbane (yes=1) 0.4922 (6.12)

11. Estimation Results using a Random Regret Mixed
Logit model (cont)
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Standard deviation of random parameters:
Modal option (BRT = 1, LRT = 0) 0.6025 (15.77)
Construction time (years) 0.3184 (27.45)
Construction cost ($m) 0.6092 (30.06)
On-board staff presence (yes=1) 0.6770 (19.02)
Risk of being closed after assured period (%) 0.01045 (23.33)
Overall environmental friendliness compared to car (%) 0.0271 (13.54)

12. Resident Preference Model (Fixed Route Length)
› Identifying gains in voter support for BRT in the presence of LRT
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BRTcosts half LRT to build
BRTcosts 75% LRT to build
BRTserves 50% more people
BRThas no negative prejudice
ROW:BRT 80%, LRT 20%
Voters are not familiar with BRT
BRTcosts half LRT to build
Voters are familiar with BRT
BRTserves 50% more people
-4% -2% 0% 2% 4% 6% 8% 10% 12% 14%
Change to support for BRT

13. Conclusions
› The simulated examples of project planning scenarios show the impact on
preferences and considerable support for BRT by sensible planning
especially
- In situations of lower construction cost
- In a dedicated corridor
› Resident preference model a complementary tool for planning and
evaluation
› The extension to include international comparison offers the opportunity to
establish whether these attributes and the consequent residential
preference model can be generalised.
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