Preview

The Russian Automobile and Highway Industry Journal

Advanced search

Assessment of traffic flow parameters on congested sections of the primary road network of the resort region

https://doi.org/10.26518/2071-7296-2026-23-4-568-581

EDN: WKNPXY

Abstract

Introduction. The article proposes an approach to determining traffic flow parameters on congested sections of the primary road network in a resort region, aimed at estimating time losses and road transport accessibility of tourist and transport hubs. Two representative sections of federal highways in Krasnodar Krai have been considered: M-4 “Don” section with a sequential reduction in the number of traffic lanes, and A-147 section within the boundaries of Dagomys settlement with controlled intersections.

Materials and Methods. The initial database was compiled from traffic management projects, data from stationary traffic detectors, navigation services, and field surveys using the floating car method. For comparison, the Bureau of Public Roads function, the Highway Capacity Manual approach, and traffic simulation modeling have been applied. 

Results. It is shown that seasonal tourist demand and commuting migration create significant unevenness in traffic intensity. On M-4 “Don” highway, the maximum monthly traffic volume in August reaches 1.35 million vehicles per month, which is 256.2% higher than the figures recorded in February during the low tourist season. On A-147 highway, traffic intensity increases to 1.36 million vehicles per month, exceeding the February level by 43.7%. During peak periods, traffic speed decreases to 8–11 km/h, while queue lengths reach 9.8 km on M-4 and 2.4 km on A-147. 

Discussion and Conclusion. Calculation methods provide a smoothed assessment of traffic conditions and underestimate travel time under congested conditions. The simulation model reproduces vehicle accumulation, queue propagation, and stop-and-go regimes; therefore, it is advisable to use it as the primary tool for analyzing congested sections and selecting targeted measures to improve network efficiency.

About the Author

R. S. Runets
“Center of Road Innovations” Limited Liability Company, “Institute of Transport Development” Limited Liability Company; Moscow Automobile and Road Construction State Technical University (MADI)
Russian Federation

Runets Roman S. - General Director, Center of Road Innovations LLC and Institute of Transport Development LLC; Postgraduate Student, Moscow Automobile and Road Construction State Technical University (MADI) 

182/4, Tamanskaya Street, Krasnodar, Krasnodar Region,350040

64, Leningradskiy Prospekt, Moscow, 125319

Scopus ID: 60608780500, Researcher ID: PYZ-2364-2026



References

1. Mohammadian S., Zheng Z., Haque M.M., Bhaskar A. Continuum modeling of freeway traffic flows: State-of-the-art, challenges and future directions in the era of connected and automated vehicles. Communications in Transportation Research. 2023;Vol. 3. 100107. https://doi.org/10.1016/j.commtr.2023.100107

2. Wang Y., Wang L., Yu X., Guo J. Capacity Drop at Freeway Ramp Merges with Its Replication in Macroscopic and Microscopic Traffic Simulations: A Tutorial Report. Sustainability. 2023; Vol. 15, no. 3. 2050. https://doi.org/10.3390/su15032050

3. Li J., Ma W. A survey on urban traffic control under mixed traffic environment with connected automated vehicles. Transportation Research Part C: Emerging Technologies. 2023; Vol. 154. 104258. https://doi.org/10.1016/j.trc.2023.104258

4. Helbing D. Traffic and related self-driven many-particle systems. Reviews of Modern Physics. 2001; Vol. 73. Pp. 1067–1141. https://doi.org/10.1103/RevModPhys.73.1067

5. Imran W., Tettamanti T., Varga B., Bifulco G.N., Pariota L. Macroscopic modeling of connected, autonomous and human-driven vehicles: A pragmatic perspective. Transportation Research Interdisciplinary Perspectives. 2024; Vol. 24. 101058. https://doi.org/10.1016/j.trip.2024.101058

6. Hącia E. The impact of tourist traffic on the functioning of Polish seaside health resorts. Transportation Research Procedia. 2016; Vol. 16. Pp. 110–121. https://doi.org/10.1016/j.trpro.2016.11.012

7. Curtale R., Morandi M., Cavagnaro E. Traffic Congestion in Rural Tourist Areas and Sustainable Mobility Solutions: A Stated Preference Approach. Journal of Sustainable Tourism. 2023; Vol. 31, no. 8. Pp. 1843–1864. https://doi.org/10.1080/09669582.2022.2108044

8. Cheng J., Wu L., Gao Y., Tian X. A multi-agent model of traffic simulation around urban scenic spots: From the perspective of tourist behaviors. Heliyon. 2023; Vol. 9. e20929. https://doi.org/10.1016/j.heliyon.2023.e20929

9. Connell J., Page S.J. Exploring the spatial patterns of car-based tourist travel in Loch Lomond and Trossachs National Park, Scotland. Tourism Management. 2008; Vol. 29, no. 3. Pp. 561–580. https://doi.org/10.1016/j.tourman.2007.06.005

10. Potapova I.A., Boyarshinova I.N., Ismagilov T.R. Methods of traffic flow modeling. Fundamental’nye issledovaniya = Fundamental Research. 2016; 10-1: 78–83 (In Russ.).

11. Nedyak A.V., Rudzeit O.Yu., Zaynetdinov A.R. Classification of traffic flow modeling methods. Vestnik Evraziyskoy nauki = The Eurasian Scientific Journal. 2019; 11(1): 14. (In Russ.) https://doi.org/10.15862/25TVN119

12. Rowan D., Fountas G., Anastasopoulos P.Ch. A systematic review of machine learning-based microscopic traffic flow models and simulations. Communications in Transportation Research. 2025; Vol. 5. 100149. https://doi.org/10.1016/j.commtr.2025.100149

13. Sunderrajan A., Viswanathan V., Cai W., Knoll A. Traffic state estimation using floating car data. Procedia Computer Science. 2016; Vol. 80. Pp. 2336–2341. https://doi.org/10.1016/j.procs.2016.05.436

14. Blumthaler W., Bursa B., Mailer M. Influence of floating car data quality on congestion identification. European Journal of Transport and Infrastructure Research. 2020; Vol. 20, no. 4. Pp. 22–37. https://doi.org/10.18757/ejtir.2020.20.4.5287

15. García-Castro A., Monzón A. Using floating car data to analyse the effects of ITS measures and eco-driving. Sensors. 2014; Vol. 14. Pp. 21358–21374. https://doi.org/10.3390/s141121358

16. Zheng L., Ma H., Wang Z. Travel Time Estimation for Urban Arterials Based on the Multi-Source Data. Sustainability. 2024; Vol. 16. 7845. https://doi.org/10.3390/su16177845

17. Bai L., Wong W., Xu P., Liu Y., Chow A.H.F., Lam W.H.K. Fusion of multi-resolution data for estimating speed–density relationships. Transportation Research Part C. 2024; Vol. 165. 104742. https://doi.org/10.1016/j.trc.2024.104742

18. Ipekyuz B., Sevinen P.K., Yaman H.T. Performance Evaluation of Fused Floating Car Data (FCD) and Bluetooth (BT) Data Speed Estimation on Urban Arterials. Arabian Journal for Science and Engineering. 2025; Vol. 50. Pp. 17091–17108. https://doi.org/10.1007/s13369-024-09844-x

19. Hale D.K., Ghiasi A., Khalighi F., Zhao D., Li X., James R.M. Vehicle Trajectory-Based Calibration Procedure for Microsimulation. Transportation Research Record. 2023; Vol. 2677, no. 1. Pp. 1764–1781. https://doi.org/10.1177/03611981221124597

20. Zhou A., Peeta S., Zhou H., Laval J., Wang Z., Cook A. Implications of Stop-and-Go Traffic on Training Learning-Based Car-Following Control. Transportation Research Part C: Emerging Technologies. 2024; Vol.163. 104578. https://doi.org/10.1016/j.trc.2024.104578


Review

For citations:


Runets R.S. Assessment of traffic flow parameters on congested sections of the primary road network of the resort region. The Russian Automobile and Highway Industry Journal. 2026;23(4):568-581. (In Russ.) https://doi.org/10.26518/2071-7296-2026-23-4-568-581. EDN: WKNPXY

Views: 138

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2071-7296 (Print)
ISSN 2658-5626 (Online)