Abstract.

The article presents an integrated modeling framework for predicting traffic and passenger flows in large city transport system based on dynamic assignment models. Besides trip distribution and modal choice, the model incorporates departure time choice and produces individual departure time distributions for each origindestination pair and transport mode. The model takes into account trip chaining and transport modes availability to produce consistent origin-destination matrices. Dynamic traffic assignment plays a crucial role in the proposed model formulation and can produce realistic time-dependent network loading with congestion spillback.

Keywords:

transport modeling, origin-destination matrix, dynamic traffic assignment

PP. 3-12.

REFERENCES

1. Aliev A. S., Mazurin D. S., Maksimova D. A., Shvetsov V. I. Struktura complexnoj modeli transportnoy sistemy g. Moskvy [The structure of the complex model of the transport system of Moscow] // Trudy ISA RAS [ISA RAS Proceedings]. 2015. Vol. 65, no. 1. P. 3–15.
2. Sheffi Y. Urban transportation networks: equilibrium analysis with mathematical programming methods. Prentice-Hall, 1985.
3. Peeta S., Ziliaskopoulos A. K. Foundations of Dynamic Traffic Assignment: The Past, the Present and the Future // Networks and Spatial Economics. 2001. Vol. 1, no. 3. P. 233–265.
4. Aliev A. S., Mazurin D. S., Shvetsov V. I. Model’ transportnyh potokov na osnove 4-shagovoj shemy s uchetom cepochek peredvizhenij [Model of traffic flows based on the 4-step scheme with the chains of trips] // Trudy ISA RAS [ISA RAS Proceedings]. 2016. Vol. 66, no. 1. P. 3–9.
5. Ben-Akiva M., Bierlaire M. Discrete choice models with applications to departure time and route choice. In Handbook of Transportation Science, 2nd edition. – Kluwer. 2003. P. 7-38.
6. Szeto W. Y., Wong S. C. Dynamic traffic assignment: model classifications and recent advances in travel choice principles // Central European Journal of Engineering. 2012. Vol. 2, no. 1. P. 1–18.
7. Gentile G. Solving a Dynamic User Equilibrium model based on splitting rates with Gradient Projection algorithms // Transportation Research Part B: Methodological. 2016. Vol. 92. P. 120–147.
8. Daganzo C. F. The cell transmission model, part II: Network traffic // Transportation Research Part B: Methodological. 1995. Vol. 29, no. 2. P. 79–93.
9. Yperman I. The Link Transmission Model for Dynamic Network Loading: Ph.D. thesis / Katholieke Universiteit Leuven. 2007.
10. Tampére C. M., Corthout R., Cattrysse D., Immers L. H. A generic class of first order node models for dynamic macroscopic simulation of traffic flows // Transportation Research Part B: Methodological. 2011. Vol. 45, no. 1. P. 289–309.
11. Spiess H., Florian M. Optimal strategies: A new assignment model for transit networks // Transportation Research Part B: Methodological. 1989. Vol. 23, no. 2. P. 83–102.
12. Gentile G., Noekel K. Modelling Public Transport Passenger Flows in the Era of Intelligent Transport Systems: COST Action TU1004 (TransITS). Springer Tracts on Transportation and Traffic. Springer International Publishing, 2016. ISBN: 978-3-319-25082-3.