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RECONSTRUCTION OF TRAFFIC FLOW DYNAMICS BASED ON DETERMINASTIC-STOCHASTIC MODEL ANSD DATA OBTAINED FROM INTELLIGENT TRANSPORT SYSTEMS
Alexander S. Bugaev, Moscow Physical Technical Institute, Dolgoprudny, Moscow region, Russia, bugaev@cplire.ru
Alexander G. Tatashev, Moscow Automobile and Road Construction State Technical University (MADI), Moscow, Russia; Moscow Technical University of Communication and Informatics (MTUCI), Moscow, Russia,
a-tatashev@yandex.ru
Marina V. Yashina, Moscow Automobile and Road Construction State Technical University (MADI), Moscow, Russia; Moscow Technical University of Communication and Informatics (MTUCI), Moscow, Russia,
yash-marina@yandex.ru
Oleg S. Lavrov, Moscow Automobile and Road Construction State Technical University (MADI), Moscow, Russia, lavrovolegs@yandex.ru
Elisey A. Nosov, Moscow Automobile and Road Construction State Technical University (MADI), Moscow, Russia
Abstract
Development of information technologies BigData makes it possible to carry out detailed measurements on highways. The congested traffic flows are very unstable and it is important to know the state function describing the dependence of the flow intensity on the density. An intelligent transport system has been created in Moscow. This system collects data on the flow characteristics in real time. In this paper, an algorithm has been developed to construct to adjust a mathematical traffic model to traffic modeling. The adjustment of the model parameters is based on the results of the 2011 year measurements on a segment of Leningradsky prospect with aid of the intelligent transport system SS125 — Tra?c Sensor Smartsensor Wavetronix. The accuracy of the 2011 year data are not high. However these data contain a per minute information on different characteristics. The 2019 year data are more accurate but only traffic flow intensity was measured for two directions of the movement. The developed algorithm is used for reconstruction of the flow density and velocity on the base of the 2011 year measurements. Different version of the considered traffic model are considered. One of these version is a new model.
Keywords:mathematical traffic model, dynamical system, exclusion stochastic processes, measurements on highway, traffic flow characteristics.
References
1. Kanai M., Nishinary K., Tokihiro T. (2009). Exact solution and asymptotic behavior of the asymmetric simple exclusion process on a ring. arXiv.0905.2795v1 [cond-mat-stat-mech] 18 May 2009.
2. Nagel K., Schreckenberg M. (1992). A cellular automation models for freeway traffic. J. Phys. I. France 2, 1992, pp. 2221-2229. https://dx.doi.org/10.1051/jp1.1992277.
3. Kanai M. Two-lane tra?c model with an exact steady-state solution-physics. Physical Review, Statistical Nonlinear and Matter Physiscs, 82(6 Ph2): 066107.
4. Buslaev A.P., Prikhodko V.M., Tatashev A.G., Yashina M.V. (2005). {\it The deterministic-stochastic flow model.} arXiv:physics/0504139[physics.sos-ph] 2005: 1-21.
5. Pospelov P., Kostsov A., Tatashev A., Yashina M. (2019). A mathematical model of traffic segregation on multilane road. Periodical of Engineering and Natural Science, vol. 7, no. 1, pp. 442-446. http://dx.doi.org/10.21533/pen.v7i1.384.
6. Pospelov P.I., Belova M.A., Kostsov A.V., Tatashev A.G., Yashina M.V. (2019). Technique of Traffic Flow Evolution Localization for Calibration of Deterministic-Stochastic Segregation Model. In 2019 Systems of Signals Generating and Processing in the Field of on Board Communications (pp. 1-5). IEEE.
7. Kozlov V.V., Buslaev A.P., Tatashev A.G. (2013). Monotonic random walks and clusters flows on networks. Models and applications, Lambert Academician Publishing, Saarbrucken, Germany, no. 78724. http://dx.doi.org/10.21533/pen.v7i1.384.
8. Buslaev A.P., Yashina M.V., Tatashev A.G. (2017). On state functions in model of heterogeneous traffic. Vestnik MADI, No. 3 (50), pp. 45-51. (In Russian)
9. Daduna H. (2001). Queuing networks with discrete time scale: explicit expression for the steady state behavior of discrete time stochastic networks. Springer-Verlag, Berlin, Heldelberg.
10. Blank M. (1970). Metric properties of discrete time exclusion type processes in continuum. J. Stat. Phys. Vol. 140, pp. 170-197.
11. Spitzer F. (1970). Interaction of Markov processes. Advances in Mathematics, vol. 5, pp. 246-290. http://dx.doi.org/10.1016/0001-8708(70)90034-4.
12. Bocharov P.P., Pechinkin A.V. (1995). Queueing theory. Moscow, RUDN, 529 p.
13. Bugaev A.S., Buslaev A.P., Kozlov V.V., Tatashev A.G., Yashina M.V. (2015). Generalized transport-logistic problem as class of dynamical system. Matematicheskoye modelirovaniye, vol. 27. No. 12, pp. 65-87.
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