Performance-based planning and programming has increased in popularity for transit project funding in recent years. This methodology focuses on quantitative performance measures to inform decision making. For transit projects, projections or observed ridership is the most commonly used performance measure to evaluate project benefits. Conventional wisdom within the transit industry suggests that measuring the performance of a transit project immediately after project opening may not capture all the project’s benefits, since it takes time for a project to realize its short-term ridership potential, a process commonly referred to as ridership ramp-up. While this idea is both intuitive and appealing, especially for projects that seem to be underperforming in their initial years, there is a need for empirical analysis to determine the typical magnitude and extent of ridership ramp up in order to better account for ramp-up in ridership forecasting and transit project evaluation. The purpose of this study is to meet this need by evaluating variations in ridership in the initial years after project opening for 55 fixed-guideway rail transit projects in the United States. I applied a fixed-effects regression model to predict one-year increases in ridership in each of the first five years after project opening, controlling for variation in gas prices, population, income, and unemployment. I find that ridership on new rail transit projects increases on average six percent controlling for other factors between the opening year and the first year after project opening. These findings can support decisions about how to account for ridership ramp up in forecasting and performance evaluation for rail transit projects.
Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3314 |
Date | 01 December 2018 |
Creators | Shinn, Jill Elizabeth |
Publisher | DigitalCommons@CalPoly |
Source Sets | California Polytechnic State University |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Master's Theses |
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