Recent Washington State Ferries (WSF) origin–destination travel survey data collection and model development activities are described. WSF performed similar activities in 1993. The lessons learned from the initial project helped to develop a better survey design and sampling plan. The overall survey response rate was 37%, about 28% higher than the response rate for the 1993 travel survey. The improved survey design and sampling plan also provided a richer data platform from which to establish more representative observed baseline ferry travel patterns, by boarding method and access and egress mode combinations. Compared with 1993, the 1999 travel survey results reflect a significant increase in the systemwide use of transit for access to and egress from ferry terminals. This increase was primarily attributed to improved transit service, timed ferry-bus connections, and ferry-bus passes implemented by Kitsap Transit and other transit agencies. In addition to the 1999 travel survey, WSF conducted a stated preference survey. The combined data from these two surveys provided information on ferry users’ revealed and stated preferences, which allowed a more rigorous statistical mode and route choice estimation analysis to be performed. This analysis helped develop a more robust and internally consistent model, as reflected in model performance. For walk-on and automobile-boarding passengers, the updated WSF model has produced more accurate route-level validation results than the previous model. The results from the model estimation and validation analyses are presented. Specific improvements made in the updated model are highlighted.
The Florida Department of Transportation Turnpike Enterprise’s recent toll mode-choice model development activities are described. Because the simple toll travel forecasting analysis methods used were not adequate for reliably addressing contemporary toll study issues, there was a need for toll modeling innovations that address trip makers’ toll route decisions as a mode-choice step sensitive to changes in service levels by time of day, trip purpose, and socioeconomic attributes. Innovations developed for Florida’s turnpike began with data-collection efforts and toll model development for the Central Florida (Orlando) region. This represents the next generation of modeling system. Similar efforts are under way for the Miami–Fort Lauderdale area. The Orlando region toll mode-choice model, which is in its final validation phase, includes a statistically estimated nested mode-choice modeling system with a discrete choice for toll travel. The models were developed for a combination of four periods and four trip purposes, including visitor trips. Other key features are ( a) a pre-mode-choice time-of-day process; ( b) a generalized cost-assignment procedure that uses travel time and costs by time of day (rather than travel time alone); ( c) production of zone-to-zone travel time and costs consistent with travel paths; and ( d) a feedback loop process that uses an iterative successive averaging procedure to estimate travel times.
Washington State Ferries operates what is by far the largest ferry system in the United States. The ferry routes serve as the primary transportation link to several islands in Puget Sound. Customers include a mixture of commuters, nonwork travelers, and tourists who use the ferries as walk-on or drive-on passengers. The system experiences seasonal and time-of-day peaks that result in some routes operating at capacity for drive-on customers. As part of a recently completed long-range planning effort, Washington State Ferries in association with the state's transportation commission wanted to evaluate the impact of changes in fare policies—in particular, to analyze the effects of charging different fares at different times of day. Fare levels have changed several times over the past several years. The resulting elasticities corresponding to these fare increases were calculated and provide a reasonable foundation for estimating the effects of future fare changes. However, the elasticities do not reflect the effects of charging different fares by time of day. To estimate those elasticities, a stated preference survey was designed and administered to current drive-on customers. Data from the survey were used to estimate both segment-level discrete choice models for choice of alternative drive-on sailings, walk-on, or shifting to an alternative route or mode. Hierarchical Bayes estimation was used to develop models that reflect random heterogeneity in preferences and those models were used in a simulation model to estimate time-of-day fare elasticities. This paper describes the resulting fare elasticities and their implications for fare policy.
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