1. May 2009 Evaluation of Time-of- Day Fare Changes for Washington State Ferries Prepared for: TRB Transportation Planning Applications Conference

2. Presentation Outline  Project background  Project objectives  Study approach  Results  Conclusions 2

3. 3  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

4. 4  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

5. 5 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

6. 6  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

7. 7  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

8. 8 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

9. 9  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

10. 10  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 Elasticity of Peak Drive-on Volume to Off-peak Fares (20% off- peak fare decrease)

11. 11  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

12. 12  Significant mode shifts can be induced by pricing changes Higher Fares with Increased Walk/Drive Differences

13. 13 Higher Fares with Increased Walk/Drive Differences  Revenue increases with drive-on fare increases up to 50%

14. 14 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. www.wsdot.wa.gov/ferries/planning/ESHB2358

15. 15 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.