evaluation of time-of-day fare changes for washington state ferries
DESCRIPTION
Evaluation of Time-of-Day Fare Changes for Washington State Ferries. Prepared for: TRB Transportation Planning Applications Conference. Presentation Outline. Project background Project objectives Study approach Results Conclusions. Washington State Ferries Background. - PowerPoint PPT PresentationTRANSCRIPT
May 2009
Evaluation of Time-of-Day Fare Changes for Washington State Ferries
Prepared for:TRB Transportation Planning Applications Conference
Presentation Outline
Project background
Project objectives
Study approach
Results
Conclusions
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Formed in 1951, is the largest ferry transit system in the U.S.
Serves about 23 million passengers and vehicle trips per year
Operates 10 ferry routes and runs nearly 500 sailings per day
Provides service to eight Washington State counties and the Province of British Columbia
Operates and maintains 20 terminals
Provides priority loading for bicycles, vanpools, and carpools
Washington State Ferries Background
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Plan is based on a 2007 legislation and needed to:
Develop strategies to minimize capital and operational costs
Implement adaptive management practices to improve service quality and keep costs at lowest possible level
Re-establish vehicle LOS standards
Plan developed jointly by WSF staff & State’s Joint Legislature Transportation Committee with input from Washington State Transportation Committee
Plan has been submitted to legislature for review and is being finalized
Washington State Ferries Long Range Plan
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Washington State Ferries Travel Demand Model
Initially developed in early 1990s and updated using 1993, 1999 & 2006 ferry travel survey data
Focuses on PM peak ferry travel
Covers 12-County service area
Uses incremental methods (as in Sound Transit model)
Relies on observed ferry travel patterns
Interfaces with PSRC model & transportation and land use data from other jurisdictions
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Evaluate the effects of fare policy changes on ferry traffic
Overall fare increases Changing car/walk-on fare differentials Time-of-day fare differentials
To do this, needed to estimate fare elasticities for:
Using the ferry Ferry submode Time-of-day shifts
Purpose of Our Work
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Designed stated preference survey to collect information on likely responses to fare and other service changes:
Four attributes: fare, waiting time, time of earlier sailing, time of later sailing
Five choice alternatives: Drive-on ferry at current sailing time Drive-on ferry at earlier sailing time Drive-on ferry at later sailing time Walk-on ferry at current sailing time Use other non-ferry alternative
Survey was administered to 840 current drive-on customers
Data from survey were used to estimate discrete choice models
Aggregate models to determine appropriate segmentations and specifications
Individual-level models estimated using mixed logit and hierarchical Bayes
Study Approach
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Some Notes on Choice Modeling Approach Market research firm that collected data conducted initial choice modeling
Used hierarchical Bayes estimation Provides individual-level utilities
Used different type of specification Attributes-only (no systematic sources of heterogeneity) Over-specified model Represented fare with eight discrete levels
Models showed much higher fare elasticity than seemed reasonable
The WSF team developed “refined” choice models Specified continuous fare utility functions Allowed non-linear income effect on fare sensitivity Reduced specifications to ones that could be identified Segmented models to allow more consistent priors Estimated models with both mixed logit and hierarchical Bayes Produced posterior (individual-level) utility functions
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Choice models indicate that drive-on customers are willing on average to shift departure times by 30 minutes for a $3 fare savings
Discretionary trips have more flexibility in departure times Some differences in flexibility among routes
Spreadsheet-based simulation used to calculate route-group and segment elasticities
Overall drive-on fare elasticity estimated using stated preference data is closely comparable to observed historical fare elasticity:
Stated preference fare elasticity: -0.30 Historical fare elasticity: -0.32
Average elasticity of fare sensitivity to income is -0.13
“Refined” Choice Model Results
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Overall elasticities to time-of-day fare changes somewhat higher than expected
Relatively high current fares Higher income customers have more time
flexibility
Elasticity Estimates by Segment
Mandatory Trips Discretionary TripsNorth Routes 0.59 0.74
Central Routes 0.52 0.65South Routes 0.34 0.49Island Routes 0.97 0.91
Overall 0.51 0.64
Elasticity of Peak Drive-on Volume to Off-peak Fares (20% off-peak fare decrease)
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Modest peak period differentials cause significant enough shifts to relieve peak hour capacity issues
Effects of Differential Time-of-Day Fares
Time-of-Day Fare Sensitivities
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Significant mode shifts can be induced by pricing changes
Higher Fares with Increased Walk/Drive Differences
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Higher Fares with Increased Walk/Drive Differences
Revenue increases with drive-on fare increases up to 50%
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The Current Washington State Ferries Plan
The Plan proposes use of a reservation system to manage demand for the limited peak-period drive-on capacity. It proposes encouraging walk-on use by increasing passenger fares at half the rate of vehicle fares.
The Plan also discusses other pricing strategies including time-of-day-based congestion pricing for “possible future consideration” after first implementing the reservation system. The Plan notes that:
The pricing strategy with the greatest potential to shift travel behavior is congestion pricing. If reservations alone are not sufficient to shift demand then it may be necessary to evaluate a reservations plus variable congestion pricing approach.
The Washington State Ferries Draft Long-Range Plan is available online at: www.wsdot.wa.gov/ferries/planning/ESHB2358. Appendix D of the Plan contains details of the time-of-day elasticity estimation process and results.
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Conclusions
Price elasticity estimates developed using stated preference survey data and properly specified discrete choice models are comparable to those estimating using historical data
Stated preference surveys can in addition be used to estimate time-of-departure and submode shifting elasticities
In the Washington State Ferries markets, the elasticity of departure time with respect to fare is reasonably high. This means that variable pricing can be a very effective means of shifting customers away from peak departure times
While the Washington State Ferries Draft Long-Range Plan does not currently propose use of variable pricing, it does say that variable pricing may be considered in the future if other demand management techniques are not sufficient to achieve system objectives.