G-EMME/2: Automatic Calibration Tool of the EMME/2 Transit Assignment Using Genetic Algorithms
Publication: Journal of Transportation Engineering
Volume 133, Issue 10
Abstract
This research presents an automatic procedure for calibrating transit-assignment model parameters. The calibration process targets the optimal set of parameter values by ensuring that the assignment output volumes match ridership volumes obtained from on-board surveys. Due to the combinatorial nature of the problem of interest, the proposed calibration process is automated using genetic algorithm techniques to find the best values for parameters through minimizing a “misfit” function. This study presents the new G-EMME/2 tool, which is an automatic calibration tool designed to find the optimal set of values for the transit-assignment model parameters implemented in the EMME/2 transportation planning software. The G-EMME/2 tool was applied to the Toronto transit network, and the five EMME/2 aggregate transit-assignment model parameters were estimated. The results are very encouraging. This research is an attempt to help automate the tedious process of calibrating transit assignment models.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research was supported by the Natural Sciences and Engineering Research Council (NSERC).
References
Farrol, B., and Livshits, V. (1998). “Analysis of individual transit trips in EMME/2: Calibration of 1996 TTC trips disaggregate assignment.” Report No. 80, Joint Program in Transportation, Univ. of Toronto.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, Mass.
INRO. (1990). EMME/2 user’s manual, Les Conseillers INRO Consultants Inc., Montreal.
Joint Program in Transportation. (1997a). “Transportation tomorrow survey 1996: Design and conduct of the survey.” Joint Program in Transportation, Univ. of Toronto.
Joint Program in Transportation. (1997b). “Transportation tomorrow survey 1996: Data validation.” Joint Program in Transportation, Univ. of Toronto.
Joint Program in Transportation. (2003). “2001 transportation tomorrow survey: 2001, 1996 and 1986 summary report of the GTA.” Joint Program in Transportation, Univ. of Toronto.
Krishnakumar, K., Narayanaswamy, S., and Garg, S. (1995). “Solving large parameter optimization problems using a genetic algorithm with stochastic coding.” Genetic algorithms in engineering and computer science, G. Winter, ed., Wiley, Chichester, N.Y., 287–304.
Ma, T., and Abdulhai, B. (2002). “Genetic algorithm-based optimization approach and generic tool for calibrating traffic microscopic simulation parameters.” Transportation Research Record. 2–2131.
Parveen, M. (2003). “Calibration of the aggregate transit assignment model of EMME/2 using genetic algorithms.” MASc thesis, Univ. of Toronto, Toronto.
Spiess, H., and Florian, M. (1989). “Optimal strategies: A new assignment model for transit networks.” Transp. Res., Part B: Methodol., 23(2), 83–102.
Vemuri, V., and Cedeño, W. (1995). “Multi-niche crowding for multimodal search.” Practical handbook of genetic algorithms: New frontiers, L. Chambers, ed., Vol. 2, CRC, Boca Raton, Fla.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 133 • Issue 10 • October 2007
Pages: 549 - 555
Copyright
© 2007 ASCE.
History
Received: Sep 8, 2005
Accepted: Nov 27, 2006
Published online: Oct 1, 2007
Published in print: Oct 2007
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.