SUPERCOMPUTER SIMULATION OF VEHICULAR TRAFFIC BASED ON QGD SYSTEM OF EQUATIONS
Abstract
The paper is devoted to the problem of modeling traffic flows under the macroscopic ap-proach. In this approach the continuous medium approximation is used, i.e. traffic is represented as compressible fluid flow; the main studied flow characteristics are density and average velocity of cars. The model based on the quasigasdynamic (QGD) system of equations is proposed in the work. The one-dimensional QGD model of vehicular flow is governed by the system of two equations: the continuity equation for density and the momentum conservation equation for average flow velocity. The equations include terms responsible for the “human factor” – the ability of drivers to accelerate and decelerate depending on traffic conditions, in particular, on the flow density in front of them. In the right-hand sides of the equations there are terms describing possible sources in the case of entry or exit or in case of changing the number of lanes. The equations also contain diffusion terms in the right-hand sides to provide the solution smoothing at distances of the order of the medium reference scales. A small parameter is introduced having the sense of the reference time that is the time interval for which several cars are crossing a given point on the road. Despite the one-dimensionality the model allows describing traffic on multi-lane roads and complex junctions qualitatively correctly, while the computation process is simplified greatly, saving computing resources. To approximate differential equations explicit two-level difference schemes of the second order on space were used. The algorithm for parallel computing is proposed on the basis of geometrical parallelism; high speed-up values are obtained. To verify the model a number of test predictions were performed; the results agreed with data obtained by other researchers. In the one-dimensional statement the next problems have been solved: the problem of rush hour traffic on a road with a side entry, the problem of traffic on the clover junction as well as the problem of the effect of the traffic light phases’ dura-tion on the appearance and dynamics of density jumps in a vicinity of the traffic light. Traffic model-ing on a real section of the Moscow city road network has been also fulfilled.
References
2. Maiyorov N.N., Romanek V.A. Voprosy vybora matematicheskikh modeley dlya issledovaniya passazhirskikh potokov v transpotnykh sistemakh [Questions of the choice of mathematical models for the study of passenger flows in transport systems], Systemnyy analiz i logistika [System Analysis and Logistics], 2017, Vol. 1, No. 14, pp. 39-45.
3. Razumov D.S., Kataev M.Yu., Shelestov A.A. Algoritmy upravleniya potokom avtotransporta v gorodskikh usloviyakh [Algorithms for traffic management in urban areas], Vestnik sovremennykh issledovanii [Bulletin of modern research], 2018, Vol. 6.1, No. 21, pp. 471-473.
4. Nurgaliev E.R. Imitatsionnoe modelirovanie ulichno-dorozhnoi seti goroda [Simulation model-ing of the city's road network], Aktual'nye napravleniya nauchnykh issledovaniy XXI veka: Teriya i praktika [Actual directions of scientific research of the XXI century: Theory and prac-tice], 2017, Vol.5, No. 7-1, pp. 172-176.
5. Treiber A., Kesting A. Traffic Flow Dynamics. Data, Models and Simulation. Springer, Berlin-Heidelberg, 2013, 503 p.
6. Kerner B. The Physics of Traffic. Springer, Berlin, 2004, 682 p.
7. Qian Y., Zeng J., Wang N., Zhang J., Wang B. A traffic flow model considering influence of car-following and its echo characteristics, Nonlinear Dynamics, 2017, Vol.89, No. 2, pp. 1099-1100.
8. Kurz V.V., Anufriev I.E. Model' avtomobil'nogo trafika s zapazdyvayushchim argumentom – issledovanie ustoychivosti na kol'ze [Delayed Car Traffic Model - Ring Stability Study], Matematicheskoe modelirovanie [Mathematical modeling], 2017, Vol. 29, No. 4, pp. 88-100.
9. Kuang H., Xu Z.P., Li X.L., Lo S.M. An extended car following model accounting for the aver-age headway effect in intelligent transportation system, Physica A: Statistical Mechanics and its Applications, 2017, Vol. 471, pp. 778-787.
10. Nagel K., Schreckenberg M. A cellular automaton model for freeway traffic, J. Phys. I France, 1992, Vol. 2, No. 12, pp. 2221–2229.
11. Cremer M., Ludwig J. A fast simulation model for traffic flow on the basis of Boolean opera-tions, Math. Comp. Simul., 1986, Vol. 28, No. 4, pp. 297-303.
12. Chmura Th., Herz B., Knorr F., Pitz Th., Schreckenberg M. A simple stochastic cellular au-tomaton for synchronized traffic flow, Physica A: Statistical Mechanics and its Applications, 2014, Vol. 405, pp. 332-337.
13. Buslaev A.P., TatashevA.G., Yashina M.V. On cellular automata, traffic and dynamical systems in graphs, Int. J. of Eng. & Techn., 2018, Vol. 7, No. 2.28, pp. 351-356.
14. Yang H., Lu J., Hu X., Jiang J. A cellular automaton model based on empirical observations of a driver’s oscillation behavior reproducing the findings from Kerner’s three-phase traffic theo-ry, Physica A: Statistical Mechanics and its Applications, 2013, Vol. 392, pp. 4009-4018.
15. Jiang H., Zhang Zh., Huang Q., Xie P. Research of vehicle flow based on cellular automaton in different safety parameters, Safety Science, 2016, Vol. 82, pp. 182-189.
16. Li X., J.-Q. Sun J.-Q. Effects of turning and through lane sharing on traffic performance at intersections, Physica A: Statistical Mechanics and its Applications, Vol. 444, pp. 622-640.
17. Gao K., Jiang R., Wang B.-H., Wu Q.-S. Discontinuous transition from free flow to synchronized flow induced by short-range interaction between vehicles in a three-phase traffic flow model, Physica A: Statistical Mechanics and its Applications, 2009, Vol. 388, No. 15–16, pp. 3233-3243.
18. Lárraga M.E., Alvarez-Icaza L. Cellular automaton model for traffic flow based on safe driv-ing policies and human reactions, Physica A: Statistical Mechanics and its Applications, 2010, Vol. 389, No. 23, pp. 5425-5438.
19. Ge H., Cheng R.J., Lei L. The theoretical analysis of the lattice hydrodynamic models for traf-fic flow theory, Physica A: Statistical Mechanics and its Applications, 2010, Vol. 389, No. 14, pp. 2825-2834.
20. Zhang G., Sun D., Liu W., Zhao M., Cheng S. Analysis of two-lane lattice hydrodynamic model with consideration of drivers' characteristics, Physica A: Statistical Mechanics and its Applications, 2015, Vol. 422, pp. 16-24.
21. Peng G., Liu Ch., Tuo M. Influence of the traffic interruption probability on traffic stability in lattice model for two-lane freeway, Physica A: Statistical Mechanics and its Applications, 2015, Vol. 436, pp. 952- 959.
22. Jin D., Zhou J., H.L. Zhang H.L., Wang C.P., Shi Z.K. Lattice hydrodynamic model for traffic flow on curved road with passing, Nonlinear Dynamics, 2017, Vol. 89, No. 1, pp. 107-124.
23. Kaur R., Sharma S. Analysis of driver’s characteristics on a curved road in lattice model, Physica A: Statistical Mechanics and its Applications, 2017, Vol. 471, pp. 59-67.
24. Chetverushkin B.N. Kinetic Schemes and Quasi-Gas Dynamic System of Equations. CIMNE, Barcelona, 2008, 328 p.
25. Sukhinova A.B., Trapeznikova M.A., Chetverushkin B.N., Churbanova N.G. Two-dimensional macroscopic model of traffic flows, Mathematical Models and Computer Simulation, 2009, Vol. 1, No. 6, pp. 669-676.
26. Samarskii A.A. The Theory of Difference Schemes. CRC Press, 2001, 786°p.
27. Sokolov P.A., Shkolina I.V., Trapeznikova M.A., Chechina A.A., Churbanova N.G. Modelirovanie dvizheniya avtotransporta na osnove KGD sistemy uravneniy s ispol’zovaniem superkomp’iuterov [Traffic simulation on the basis of the QGD system of equations using su-percomputers], T-Comm – Telekommunikatsii i transport [T-Comm – Telecommunications and transport], 2019, No. 6, pp. 46-52.