A methodology for incorporating fuel price impacts into short-term transit ridership forecasts.
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2009-08-01
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Edition:Research report.
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Abstract:Anticipating changes to public transportation ridership demand is important to planning for and meeting ; service goals and maintaining system viability. These changes may occur in the short- or long-term; ; extensive academic work has focused on bettering long-term forecasting procedures while improvements ; to short-term forecasting techniques have not received significant academic attention. This dissertation ; combines traditional forecasting approaches with multivariate regression to develop a transferable shortterm ; public transportation ridership forecasting model that incorporates fuel price as a prediction ; parameter. The research herein addresses 254 US transit systems from bus, light rail, heavy rail, and ; commuter rail modes, and uses complementary methods to account for seasonal and non-seasonal ; ridership fluctuations. Models were built and calibrated using monthly data from 2002 to 2007 and ; validated using a six-month dataset from early 2008. Using variable transformations, classical data ; decomposition techniques, multivariate regression, and a variety of forecasting model validation measures, ; this work establishes a benchmark for future research into transferable transit ridership forecasting model ; improvements that may aid public transportation system planners in an era when, due to fuel price ; concerns, global warming and green initiatives, and other impetuses, transit use is seeing a resurgence in ; popularity.
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Main Document Checksum:urn:sha-512:6bf1463ad4f3ed44c27803e181f218e098e81657045d3e4667a9188be44a2d4a5e9622e374f09d04dccaddce3cd7df9dad1056fc418b5653e61769c4962954c5