Master of Science (MS)
Civil and Environmental Engineering
Number of Pages
Transportation related problems have become one of the most pressing and visible concerns of urban life. Growing traffic congestion increases the pressure on the transportation system and results in deterioration of both the mobility and quality of transportation service provided. Although the transportation problem is multifaceted, a very important transportation issue in the mind of the public is congestion. Limited resources and insufficient transportation capacity aggravate the problem. This necessitates optimal allocation of existing and new resources. The objective of this research is to develop a decision support system to facilitate transportation planning and policy development. The system is designed to help identify travel demand characteristics in urban areas and to evaluate key variables which influence these characteristics. In turn, this will assist policy development for better allocation of existing resources, deployment of new resources, enhancement of urban mobility and payment for access to transportation services in metropolitan areas. An urban travel demand forecasting model (TRANPLAN) and a Geographic Information System (ARC/INFO) program are used to support the analysis. The decision support system is a user friendly, menu driven analytical system. Specifically, it helps identify the spatial distribution of travel demand at the Traffic Analysis Zone level for various trip purposes. It also helps perform simple statistical analyses and incorporates Boolean algebra based analysis. Among the most powerful features of the system are its user friendly interface and its graphical display capabilities. The Las Vegas metropolitan area is used as a case study for demonstration of the application of the decision support system.
Analysis; Based; Decisions; Demand; GIS; Support; Travel
Civil engineering; City planning
University of Nevada, Las Vegas
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Burli, Sandeep, "A GIS-based decision support for travel demand analysis" (1995). UNLV Retrospective Theses & Dissertations. 526.