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On March 3, California High Speed Rail Authority (CHSRA) Executive Director Mehdi Morshed released a memorandum that describes the model the authority used to develop ridership and revenue forecasts for the state’s high-speed rail system.

During the past month, members of the public and local media have questioned CHSRA’s ridership and revenue forecasts, which estimate that 41 million people would ride the Los Angeles-to-San Francisco segment of the line by 2030.

Developed by Cambridge Systematics Inc., the ridership and revenue study took about two years to complete and was finished in February 2007. The study forecasted future travel patterns and demand as a function of variables such as population and employment, travel time and cost, fuel costs, and rail and airline schedules.

CHSRA has used the results from the model in several ways. Ridership and revenue forecasts were used to assess transportation, air quality and growth-inducing impacts of the various high-speed network alternatives studied as part of the Bay Area-to-Central Valley Programmatic Environmental Impact Report/Environmental Impact Statement (EIR/S). The authority also used the forecasts to determine how well a potential alignment or station located within the Bay Area-to-Central Valley EIR/S could maximize ridership and revenue potential.

Model forecasts also have been used in ongoing statewide planning for the high-speed system. Since 2008, CHSRA has applied the same model under different variable assumptions to develop business plans and conduct regional environmental/engineering processes.

Meanwhile, the forecasting study incorporated a “robust” peer review process at multiple stages of model development, Morshed said in the memorandum. The panel comprises international modeling and high-speed rail experts from academia, public agencies and the private sector, who provided technical guidance for the model design and development, and the resulting ridership and revenue forecasts.

CHSRA is planning additional ridership and revenue forecasts that will build on the previous efforts, incorporate model refinements that provide additional functionality and assist the authority with “understanding the magnitude and nature of ridership and revenue risk due to the inherent uncertainty with the future levels for the input variables,” according to the memorandum.