Modeling transport intersections of city streets with adaptive traffic control

Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy


Release:

2022. Vol. 8. № 1 (29)

Title: 
Modeling transport intersections of city streets with adaptive traffic control


For citation: Andronov R. V., Leverents E. E., Maltseva T. V. 2022. “Modeling transport intersections of city streets with adaptive traffic control”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 8, no. 1 (29), pp. 144-160. DOI: 10.21684/2411-7978-2022-8-1-144-160

About the authors:

Roman V. Andronov, Cand. Sci. (Tech.), Associate Professor, Department of Automobile Roads and Airfields, Tyumen Industrial University; aroma77777@mail.ru; ORCID: 0000-0001-6574-8456

Evgeniy E. Leverents, Senior Lecturer, Department of Structural Mechanics, Tyumen Industrial University;  leverentsee@tyuiu.ru; ORCID: 0000-0001-6574-8456

Tatyana V. Maltseva, Dr. Sci. (Phys.-Math.), Professor, Department of Structural Mechanics, Tyumen Industrial University;  maltsevatv@tyuiu.ru; ORCID: 0000-0002-0274-0673

Abstract:

The introduction of automated traffic control systems on the road network will improve the efficiency of its work. This will lead to an increase in throughput and improve the uniformity of traffic parameters. The number of stops, braking, wear of the undercarriage of cars, consumption of engine oil, fuel will decrease, and the environmental burden on the environment will be reduced.

Modeling transport intersections is one of the fundamental methods for studying the functioning of the road network. This method is used in all types of work related to the improvement, modi­fication, reconstruction, and expansion of the road network. Modern techniques for modeling traffic intersections allow recreating the movement of all road users through them, and predicting the results for many years to come, considering changes in both external factors (number of users for crossing) and internal (crossing configuration).

To evaluate the efficiency of the intersection, the initial data are taken from field observations. These include a random value of the traffic flow; pedestrian flows; traffic light cycle length; proximity to other intersections; the length of the transport queue when a permissive traffic light signal is given. The analysis of the obtained data makes it possible to assess the current situation on the road network and does not allow to make a forecast for the future when the parameters of the transport intersection change. This is where intersection modeling comes in handy. The intersection model allows predicting the operation of a traffic intersection, considering changes in both the entire road network and particular changes in the traffic intersection itself.

The article proposes a transport intersection model based on experimental data obtained during a full-scale experiment, as well as taking into account the uneven throughput. In relation to a traffic intersection, the capacity unevenness index affects the overall capacity of the intersection, traffic safety through the intersection, the magnitude of traffic delays, the magnitude of traffic losses, and the number of maneuvers in the stream. In addition, the introduction of the non-uniformity parameter will make it possible to predict the magnitude of transport delays and queues more accurately.

The proposed model can be used to create a set of measures to improve the road network of large and major cities, a decision to rebuild a transport intersection, or build an interchange in its place.

References:

  1. Andronov R. V., Leverents E. E., Genze D. A., Legostaeva E. N. 2020. “Influence of the organization of traffic at the controlled intersection on the uniformity of traffic capacity”. Bulletin of Civil Engineers, no. 6 (83), pp. 179-185. [In Russian]

  2. Andronov R. V. 2007. “Modeling queues at controlled intersections of the road network of a large city in a dense traffic flow”. Cand. Sci. (Tech.) diss. Tyumen. 187 p. [In Russian]

  3. Andronov R. V., Elkin B. P., Genze D. A. 2015. “The concept of congestion and the formation of queues at a controlled intersection in a dense traffic flow”. Scientific and technical bulletin of the Volga region, no. 1, pp. 39-41. [In Russian]

  4. Andronov R. V., Leverents E. E. 2021. “Application of statistical modeling to assess the effectiveness of adaptive regulation and reconstruction of intersections of the street‑road network”. Architecture, construction, transport, no. 3, pp. 40-49. [In Russian]

  5. Testeshev A. M., Testeshev A. A., Loshchinina I. E. 2016. “Remote monitoring of traffic flows in Tyumen under various conditions of the road surface on the basis of satellite observation”. Actual problems of architecture, construction, energy efficiency and ecology — 2016: Collection of materials of the international scientific and practical conference: in 3 volumes (Tyumen, 27-29 April 2016), vol. 1, pp. 167-173. [In Russian]

  6. El Zarwi F., Vij A., Walker J. L. 2017. “A discrete choice framework for modeling and forecasting the adoption and diffusion of new transportation services”. Transportation Research Part C: Emerging Technologies, vol. 79, pp. 207-223. DOI: 10.1016/j.trc.2017.03.004

  7. Hongsheng Qi, Xianbiao Hu. 2019. “Monte Carlo Tree Search-based intersection signal optimization model with channelized section spillover”. Transportation Research Part C: Emerging Technologies, vol. 106, pp. 281-302. DOI: 10.1016/j.trc.2019.07.017

  8. Lidén T., Joborn M. 2017. “An optimization model for integrated planning of railway traffic and network maintenance”. Transportation Research Part C: Emerging Technologies, vol. 74, pp. 327-347. DOI: 10.1016/J.TRC.2016.11.016

  9. Nyantakyi E. K., Adams C. A., Borkloe J. K., Pobee D. 2013. “Synchronization of signalised intersections: A case study of three major intersections on the 24th February Road, Kumasi, Ghana”. International Journal of Engineering Research and Applications, vol. 3, iss. 4, pp. 2566-2590. [In Russian]

  10. Seunghyeon Lee, Dong Ngoduy, Keyvan-Ekbatani M. 2019. “Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways”. Transportation Research Part C: Emerging Technologies, vol. 106, pp. 360-377. DOI: 10.1016/j.trc.2019.07.023

  11. Tiancheng Ruan, Linjie Zhou, Hao Wang. 2021. “Stability of heterogeneous traffic considering impacts of platoon management with multiple time delays”. Physica A: Statistical Mechanics and its Applications, vol. 583, p. 126294. DOI: 10.1016/j.physa.2021.126294