Last Thursday, the Senate Transportation and Housing Committee held yet another oversight hearing on California’s High Speed Rail project. The focus of this hearing was to discuss UC Berkeley’s scathing analysis of the ridership model used by the High Speed Rail Authority (Authority). The ridership model the Authority has used is the basis for both the environmental review work currently underway and the Business Plan.
As part of the Authority’s environmental review documents, ridership data is crucial to determining how many trains are needed, and where best to place the tracks, to ensure Californians will ride the train enough to cover the costs of operating the system. Within the Authority’s Business Plan, which is an exceptionally important document outlining the Authority’s plans for funding this mega-project, ridership data establishes how and where the train can be operationally self sufficient, and how much money will be needed from the Federal government and private investors.
In no uncertain terms, the accuracy of the ridership data is crucial. For this reason, once questions regarding the quality of the ridership modeling arose, the Senate asked UC Berkeley for an independent review. As part of their very technical presentation to the Transportation and Housing Committee on the results of that review, Samer Madanat from Berkeley and David Brownstone from Irvine concluded:
- Parameters of the CA HSR demand models are biased, which leads to biased ridership forecasts
- Comparison of Altamont vs. Pacheco [is] tainted by incorrect adjustment of the headway parameters and pre-assignment of travelers to stations
- Variance of ridership forecasts are certainly understated and likely very large
- Models should be revised to minimize bias and reduce variance of forecasts.
In short, the ridership modeling needs to be redone. California has only one chance to build high speed rail the right way. The State Senate is asking the tough questions, and now is the time for them to insist on correct answers that are not based on faulty data.