Two-dimensionally constrained disaggregate trip generation, distribution and mode choice model: Theory and application for a Swiss national model

M. Vrtic, P. Fröhlich, N. Schüssler, K.W. Axhausen, D. Lohse, C. Schiller and H. Teichert

Transportation Research Part A: Policy and Practice, 2007, vol. 41, issue 9, 857-873

Abstract: The Swiss federal government has asked the IVT, ETH Zürich in collaboration with the TU Dresden and Emch + Berger, Zürich to estimate origin-destination matrices by mode and purpose for the year 2000. The complex zoning system employing about 3000 zones required an algorithm which is fast, but also able to face generation, distribution and mode choice simultaneously. The EVA algorithm developed by Lohse et al. [Lohse, D., Teichert, H., Dugge, B., Bachner, G., 1997. Ermittlung von Verkehrsströmen mit n-linearen Gleichungssystemen unter Beachtung von Nebenbedingungen einschließlich Parameterschätzung (Verkehrsnachfragemodellierung: Erzeugung, Verteilung, Aufteilung). Schriftenreihe des Instituts für Verkehrsplanung und Straßenverkehr, H. 5/1997, Fakultät Verkehrswissenschaften "Friedrich List", Technische Universität Dresden] was adapted for this purpose. The key properties of the algorithm are a disaggregate description of the demand, and its use of appropriate logit-type models for the demand distribution, while maintaining the known marginal distributions of the matrices generated. The algorithm calculates trip production and attractions by zone using activity pairs. The combined destination and mode choice models are estimated for the different traveller types and activity pairs. The paper derives and describes for the first time the EVA algorithm in English, including the solution method used. Second, it summarises the results of choice model estimation providing generalised cost elasticities of demand by purpose and traveller type. Third, it discusses the quality of the results by assessing the structure of the matrix against actual census data for road and rail traffic.

Date: 2007
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