Optimizing High-Speed Rail Routes using a Spatial Decision Support System (SDSS): The Texas Urban Triangle (TUT) Case

Kim, H., Wunneburger, D., Neuman, M. and Ahn, S. 2014. Optimizing High-Speed Rail Routes using a Spatial Decision Support System (SDSS): The Texas Urban Triangle (TUT) Case. Journal of Transport Geography. 34, pp. 194-201. https://doi.org/10.1016/j.jtrangeo.2013.11.014

TitleOptimizing High-Speed Rail Routes using a Spatial Decision Support System (SDSS): The Texas Urban Triangle (TUT) Case
AuthorsKim, H., Wunneburger, D., Neuman, M. and Ahn, S.
Abstract

Previously, an analytical method was derived to model a possible high-speed rail route within the Texas Urban Triangle (TUT) area. By implementing a raster-based geographic information system (GIS), researchers were able to optimize a route with the given variables. Based on prior research outcomes, the authors improved the overall analytical approach. This article expands the study boundary from Travis County through Bexar County to test the corridor between the San Antonio and Austin metropolitan areas. Nearby counties are also included, resulting in a total of six counties. In addition, a statistical tool, explanatory factor analysis (EFA), is added to fill the gap identified in the previous modeling practices. The project addresses transportation corridor planning in the Texas Urban Triangle at the regional scale. Its aim is to determine the most suitable locations for new transport infrastructure by employing a Spatial Decision Support System (SDSS). The proposed SDSS provides a composite foundation for decision making to attain enhanced regional mobility, especially for a high-speed rail (HSR). Route alternatives are compared in terms of construction estimates, anticipated land acquisition fees, and suitability scores.

KeywordsGIS, geographic information systems, SDSS, spatial decision support system, regional planning, high speed rail, suitability analysis, spatial analytics, Texas planning
JournalJournal of Transport Geography
Journal citation34, pp. 194-201
ISSN0966-6923
Year2014
PublisherElsevier
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jtrangeo.2013.11.014
Publication dates
Published06 Jan 2014

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