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Development of short-term operational planning model for transit service analysis Pendyala, Ram M ; Ubaka, Ike

By: Contributor(s): Publication details: Transportation Research Record, 2000Description: nr 1735, s. 43-50Subject(s): Bibl.nr: VTI P8167:1735Location: Abstract: The subject is an integrated model of transit demand and supply that accounts for the interrelationships between ridership and service. The model consists of a set of equations in which ridership is predicted as a function of service and then service is predicted as a function of demand. In addition, the model accounts for interroute relationships by considering the competing or complementary nature of various routes within a transit system. The model proceeds through a series of iterative demand-and-supply computations to determine the appropriate service parameters and corresponding ridership levels for individual transit routes. The input variables include socioeconomic, demographic, and transit system data. The model has been implemented within a user-friendly, menu-driven software environment and has been applied on a test-case basis in the Volusia County area of Florida. The case-study results are very encouraging--the model predicted ridership on individual routes with an average absolute percent error of 24% despite the lack of high-quality data. Further refinements and validation tests of the model are planned for the future.
Item type: Reports, conferences, monographs
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Statens väg- och transportforskningsinstitut Available

The subject is an integrated model of transit demand and supply that accounts for the interrelationships between ridership and service. The model consists of a set of equations in which ridership is predicted as a function of service and then service is predicted as a function of demand. In addition, the model accounts for interroute relationships by considering the competing or complementary nature of various routes within a transit system. The model proceeds through a series of iterative demand-and-supply computations to determine the appropriate service parameters and corresponding ridership levels for individual transit routes. The input variables include socioeconomic, demographic, and transit system data. The model has been implemented within a user-friendly, menu-driven software environment and has been applied on a test-case basis in the Volusia County area of Florida. The case-study results are very encouraging--the model predicted ridership on individual routes with an average absolute percent error of 24% despite the lack of high-quality data. Further refinements and validation tests of the model are planned for the future.

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