Digital Training Platform for Comprehensive Traffic Simulation

Authors

  • A. Chechina Keldysh Institute of Applied Mathematics RAS, Moscow, Russia
  • N. Churbanova Keldysh Institute of Applied Mathematics RAS, Moscow, Russia and Moscow Automobile and Road Construction State, Technical University, Moscow, Russia
  • A. Garibyan Moscow Automobile and Road Construction State, Technical University, Moscow, Russia
  • M. Trapeznikova Keldysh Institute of Applied Mathematics RAS, Moscow, Russia and Moscow Automobile and Road Construction State, Technical University, Moscow, Russia

DOI:

https://doi.org/10.3991/ijoe.v17i13.27375

Keywords:

macroscopic and microscopic models, cellular automata model, user inter-face, visualization.

Abstract


The paper deals with the development of software for traffic flow simulation combining the widest spectrum of mathematical approaches used in this field. Macro- and microscopic models, models of cellular automata as well as different numerical methods of their computer implementation are incorporated into a digital platform. Original developments of the authors of the paper  such as quasi-gas dynamic traffic model and multilane cellular automata model take the main place. Potential users of the software are students and researchers. The platform possesses an intuitive graphical interface ensuring interactivity. Microsoft Visual Studio with C# is chosen as the development environment, the Unity 3D engine is employed for visualization and collaboration with WinForm projects. In the future, the platform can be transformed into a network computer laboratory providing access to information resources via  Internet.
Cover iJOE Vol. 17 No. 13 (2021)

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Published

2021-12-06

How to Cite

Chechina, A., Churbanova, N., Garibyan, A., & Trapeznikova, M. (2021). Digital Training Platform for Comprehensive Traffic Simulation. International Journal of Online and Biomedical Engineering (iJOE), 17(13), pp. 157–165. https://doi.org/10.3991/ijoe.v17i13.27375

Issue

Vol. 17 No. 13 (2021)

Section

Short Papers

License

Copyright (c) 2021 A. Chechina, N. Churbanova, A. Garibyan, M. Trapeznikova

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