Title page for ETD etd-10052012-153519


Type of Document Master's Thesis
Author Ramesh Chirania, Saloni
Author's Email Address saloni@vt.edu
URN etd-10052012-153519
Title Forecasting Model for High-Speed Rail in the United States
Degree Master of Science
Department Civil Engineering
Advisory Committee
Advisor Name Title
Trani, Antoino A. Committee Chair
Abbas, Montasir M. Committee Member
Hobeika, Antoine G. Committee Member
Keywords
  • TSAM
  • American Travel Survey
  • Box-Cox
  • Travel Demand Forecast
  • AMTRAK
  • High-Speed Rail
Date of Defense 2012-10-02
Availability unrestricted
Abstract
A tool to model both current rail and future high-speed rail (HSR) corridors has been presented in this work. The model is designed as an addition to the existing TSAM (Transportation System Analysis Model) capabilities of modeling commercial airline and automobile demand. TSAM is a nationwide county to county multimodal demand forecasting tool based on the classical four step process. A variation of the Box-Cox logit model is proposed to best capture the characteristic behavior of rail demand in US. The utility equation uses travel time and travel cost as the decision variables for each model. Additionally, a mode specific geographic constant is applied to the rail mode to model the North-East Corridor (NEC). NEC is of peculiar interest in modeling, as it accounts for most of the rail ridership. The coefficients are computed using Genetic Algorithms. A one county to one station assignment is employed for the station choice model. Modifications are made to the station choice model to replicate choices affected by the ease of access via driving and mass transit. The functions for time and cost inputs for the rail system were developed from the AMTRAK website. These changes and calibration coefficients are incorporated in TSAM. The TSAM model is executed for the present and future years and the predictions are discussed. Sensitivity analysis for cost and speed of the predicted HSR is shown. The model shows the market shift for different modes with the introduction of HSR. Limited data presents the most critical hindrance in improving the model further. The current validation process incorporates essential assumptions and approximations for transfer rates, short trip percentages, and access and egress distances. The challenges for the model posed by limited data are discussed in the model.

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