DOI: https://doi.org/10.32515/2664-262X.2023.8(39).1.183-195

Study of the Mathematical Model of the Stability of the Traffic Flow in the Sections of the Road Network of the City

Oleksiy Goryayinov, Anna Kozenok, Nataliia Berezhna, Igor Sysenko, Inna Babych, Olexsiy Voytov

About the Authors

Oleksiy Goryayinov, Associate Professor, PhD in Technics (Candidate of Technics Sciences), State Biotechnological University, Kharkiv, Ukraine, e-mail: goryainov@ukr.net, ORCID ID: 0000-0002-5967-2835

Anna Kozenok, Associate Professor, PhD in Technics (Candidate of Technics Sciences), State Biotechnological University, Kharkiv, Ukraine, e-mail: anna13kozenok@gmail.comm, ORCID ID: 0000-0002-3152-2253

Nataliia Berezhna, Associate Professor, PhD in Technics (Candidate of Technics Sciences), State Biotechnological University, Kharkiv, Ukraine, ORCID ID: 0000-0001-8740-3387

Igor Sysenko, Associate Professor, PhD in Technics (Candidate of Technics Sciences), State Biotechnological University, Kharkiv, Ukraine, e-mail: goryainov@ukr.net, ORCID ID: 0000-0002-5967-2835

Inna Babych, Senior Lecturer, State Biotechnological University, Kharkiv, Ukraine, ORCID ID: 0000-0003-0005-7640

Olexsiy Voytov, Graduate student , State Biotechnological University, Kharkiv, Ukraine, ORCID ID: 0000-0001-8716-2667

Abstract

The paper presents the results of the research of the mathematical model of the stability of the traffic flow on the sections of the city's street and road network when external factors change. Based on the modeling results, it is proposed to divide all factors affecting the stability of the traffic flow into three groups. The first group of factors characterizes the construction of the vehicle: the length of the vehicle, the weight of the vehicle, the power of the engine. The second group of factors is called time factors, which take into account: the driver's reaction time to a change in the road situation; time for a maneuver that can be used by the car in case of a change in the road situation; the total time of delays while driving along the route. The third group of factors takes into account the peculiarities of the construction of the infrastructure of the road environment. Such factors include: the number of traffic lanes on the roadway; the number of pedestrian crossings and traffic lights. Based on the results of modeling, a rating of factors affecting the amount of traffic flow stability margin is presented. The simulation results proved that, in the first place, the impact on the loss of stability of the traffic flow is the time for the maneuver that the car can use in the event of a change in the road situation and the number of pedestrian crossings and traffic lights on the controlled section of the road. When the listed factors change, the robustness criterion has minimal values. In second place in terms of influence on is a group of factors that take into account the driver's reaction time to a change in the road situation and the number of traffic lanes on the roadway. In third place is a group of factors that take into account the length of the car and the weight of the car. It is proved that the mathematical model of traffic flow stability has limitations regarding its application. The limitations are related to the definition of the initial data for the simulation. It is necessary to determine the flow density on the controlled section of the road network. In addition, statistically determined values are the reaction time of drivers to a change in the road situation and the presence of pedestrian crossings and traffic lights on the controlled section of the road.

Keywords

traffic flow, modeling, dynamic model, density gradient, speed gradient, amplification factor, time constant, stability criterion, traffic flow robustness criterion

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References

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16. Mittal, U., Chawla, P., & Tiwari, R. (2023). EnsembleNet: A hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models. Neural Computing and Applications, 35(6), 4755-4774. https://doi.org/10.1007/s00521-022-07940-9

17. Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C. W. (2023). Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Future Generation Computer Systems, 139, 100-108. https://doi.org/10.1016/j.future.2022.09.018

18. Xu, X., Jin, X., Xiao, D., Ma, C., & Wong, S. C. (2023). A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction. Journal of Intelligent Transportation Systems, 27(1), 1-18. https://doi.org/10.1080/15472450.2021.1977639

19. Zhu, Y., Wu, Q. & Xiao, N. (2022). Research on highway traffic flow prediction model and decision-making method. Sci Rep 12, 19919. https://doi.org/10.1038/s41598-022-24469-y

Citations

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Copyright (c) 2023 Oleksiy Goryayinov, Anna Kozenok, Nataliia Berezhna, Igor Sysenko, Inna Babych, Olexsiy Voytov