FORECASTING HIGH-SPEED RAIL RIDERSHIP

Abstract

Advantages and disadvantages of various high-speed rail (HSR) ridership forecasting approaches are summarized, and a recommended forecasting approach is presented. The recommended approach involves the use of separate relationships to estimate the diversion from each existing mode to HSR. This approach makes use of the behavioral information travelers have already provided by their revealed preferences to use existing modes for intercity trips. The choice of current modes for specific trip purposes reveals a great deal about how individuals value the attributes of that mode relative to other modes. This information is also of use in estimating induced demand. The approach presented here has been used in forecasting HSR ridership and revenue in Florida, Texas, and the Northeast Corridor. To illustrate how different factors influence the demand for HSR, model results are presented along with implied values of time and selected demand elasticities. The variation between market segments for the various components of travel time and cost is strong evidence that this approach is necessary for forecasting HSR ridership. The resulting models are also shown to be transparent in providing design information for new mode applications that can be used to maximize ridership, revenue, or the public benefits that justify public subsidies for the new modes.

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