Computational experience with an application of a simultaneous transportation equilibrium model to urban travel in Austin,Texas |
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| Affiliation: | 1. Instituto Universitario de Desarrollo Regional, Universidad de La Laguna, Facultad de CC Económicas y Empresariales, Campus de Guajara, 38071 San Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain;2. Campus de Excelencia, Universidad de La Laguna, Santa Cruz de Tenerife, Spain;1. Industry Solutions (Logistics, T&T and BAO), IBM Research – China, Beijing 100193, China;2. Department of Industrial & Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802, USA;1. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, No. 1, Zhanlanguan Road, Xicheng District, Beijing, 100044, China;2. The Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China;1. Dept. of Urban Management, Graduate School of Engineering, Kyoto University, C-1 Kyotodaigaku Katsura, Nishikyo, Kyoto 615-8540, Japan;2. Dept. of Civil Engineering, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Bandung 40132, Indonesia |
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| Abstract: | Safwat and Magnanti (1988) have developed a combined trip generation, trip distribution, modal split, and traffic assignment model that can predict demand and performance levels on large-scale transportation networks simultaneously, i.e. a simultaneous transportation equilibrium model (STEM). The major objective of this paper is to assess the computational efficiency of the STEM approach when applied to an urban large-scale network, namely the urban transportation system of Austin, Texas. The Austin network consisted of 520 zones, 19,214 origin-destination (O-D) pairs, 7,096 links and 2,137 nodes. The Central Processing Unit (CPU) time on an IBM 4381 mainframe computer was 430 seconds for a typical iteration and about 4,734 seconds, less than 79 minutes, to arrive at a reasonably accurate solution in no more than 10 iterations. The computational time at any given iteration is comparable to that of the standard fixed demand traffic assignment procedure. These results encourage further applications of the STEM model to large urban areas. |
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