Methodology for justifying the reconstruction of automobile roads

published:
Number: Issue 33(2026)
Section: Construction and civil engineering
The page spacing of the article: 143-450
Keywords: automobile road, traffic intensity, free headways, capacity, operational efficiency, reconstruction.
How to quote an article: Sergiy Neizvestniy, Liudmyla Bondarenko. Methodology for justifying the reconstruction of automobile roads. Dorogi і mosti [Roads and bridges]. Kyiv, 2026. Issue 33. P. 143–150 [in Ukrainian].

Authors

National Transport University, Kyiv, Ukraine
http://orcid.org/0000-0002-8239-065X
M.P. Shulgin State Road Research Institute State Enterprise – DerzhdorNDI SE, Kyiv, Ukraine
https://orcid.org/0000-0002-8888-313X

Summary

Introduction. The intensive development of road transport is accompanied by an increasing load on the existing road network, which leads to a gradual deterioration of traffic conditions, a reduction in average travel speeds, and an increase in delays. Under such conditions, the timely determination of the moment when road sections lose their operational efficiency and the justification of the feasibility of their reconstruction become particularly important.

Problem Statement. Most existing approaches to assessing the capacity of automobile roads are based on comparing actual traffic intensity with нормативні (standard) values and do not fully account for the influence of intersections, access points, and traffic flow structure. This results in limited accuracy when determining the real level of operational efficiency of road sections.

Purpose. The purpose of this study is to develop a methodology for determining the need for reconstruction of an automobile road section based on the analysis of free traffic headways and forecasting changes in traffic intensity over time.

Materials and Methods. The proposed methodology is based on a comprehensive combination of field observations, analytical calculations of the number of free traffic headways, determination of the maximum allowable traffic intensity, and forecasting transport demand using a logistic model. To assess operational efficiency, a criterion of the limiting number of free time headways in the traffic flow is applied.

Results. An approach is proposed for determining the limiting traffic intensity at which traffic conditions on an automobile road section cease to be efficient. The methodology makes it possible to establish the period of effective operation of a road section and to forecast the moment from which reconstruction becomes advisable.

Conclusion. The results of the study can be used for planning the reconstruction of automobile roads, improving traffic management, and substantiating engineering decisions at the stage of long-term development of the road network.

References

  1. Eser, A. Vehicle Ownership Statistics. Available at: URL: https://zipdo.co/vehicle-ownership-statistics/?utm_source=chatgpt.com#sources.
  2. Denysenko, O. (2020). Novyi pidkhid do vyznachennia propusknoi zdatnosti nerehulovanykh perekhrest [A new approach to determining the capacity of unsignalized intersections]. Systemy upravlinnia, navihatsii ta zviazku, 1(59). DOI: https://doi.org/10.26906/SUNZ.2020.1.045 [in Ukrainian].
  3. Nahliuk, I. S., Makarychev, A. V., Horbachov, P. F., & Horbachova, O. A. (2018). Vyznachennia propusknoi zdatnosti smuhy rukhu na avtomobilnykh dorohakh i miskykh vulytsiakh [Determination of traffic lane capacity on highways and urban streets]. Avtomobilnyi transport, 42, 89–97. DOI: https://doi.org/10.30977/AT.2219-8342.2018.42.0.89 [in Ukrainian].
  4. Guerrieri, M., Mauro, R., Pompigna, A., & Isaenko, N. (2021). Road design criteria and capacity estimation based on autonomous vehicles performances: First results from the European C-Roads platform and A22 motorway. Transport and Telecommunication Journal, 22(2), 230–243. DOI: https://doi.org/10.2478/ttj-2021-0018.
  5. Neizvestnyi, S. V., & Palchyk, A. M. (2025). Simulation of vehicle movement at unregulated intersections of public roads. Modern Science – Moderní věda, 1. DOI: https://doi.org/10.62204/2336-498X-2025-1-17.
  6. Neizvestnyi, S. V., & Palchyk, A. M. (2020). Analiz metodiv, yaki vykorystovuiutsia pry obgruntuvanni rekonstruktsii avtomobilnykh dorih [Analysis of methods used to justify road reconstruction]. Dorohy i mosty, 21, Р. 70–76. DOI: https://doi.org/10.36100/dorogimosti2020.21.070 [in Ukrainian].
  7. Liu, R., & Shin, S.-Y. (2025). A review of traffic flow prediction methods in intelligent transportation system construction. Applied Sciences, 15(7), 3866. https://doi.org/10.3390/app15073866
  8. Chernenko, A. O., Khalipova, N. V., & Lesnikova, I. Yu. (2016). Shchodo modeliuvannia transportnykh potokiv dlia analizu zavantazhenosti dorih v mistakh [On traffic flow modeling for urban road congestion analysis]. Transportni systemy ta tekhnolohii perevezen, 12, 90–98. DOI: https://doi.org/10.15802/tstt2016/85890 [in Ukrainian].
  9. Neizvestnyi, S. V., Palchyk, A. M., Neizvestna, N. V., & Dodukh, K. M. (2021). Rozroblennia zakhodiv z pokrashchennia dorozhnikh umov na diliankakh avtomobilnykh dorih na osnovi analizu umov rukhu [Development of measures to improve road conditions based on traffic analysis]. Dorohy i mosty, 24, 159–168. DOI: https://doi.org/10.36100/dorogimosti2021.24.159  [in Ukrainian].
  10. Hryhorova, T. M., & Dashchenko, A. F. (2007). Metody ta praktyka prohnozuvannia rozrakhunkovykh kharakterystyk avtomobilnykh dorih [Methods and practice of forecasting road performance indicators]. Proceedings of Odessa Polytechnic University, 1(27), 51–56 [in Ukrainian].
  11. Yerina, A. M. (2001). Statystychne modeliuvannia ta prohnozuvannia [Statistical modeling and forecasting]. Kyiv: KNEU. 170 p. [in Ukrainian].
  12. Neizvestnyi, S. V. (2023). Vyznachennia periodu efektyvnoho funktsionuvannia avtomobilnoi dorohy [Determination of the effective operation period of a road]. Dorohy i mosty, 27, 245–252. DOI: https://doi.org/10.36100/dorogimosti2023.27.245 [in Ukrainian].
  13. Neizvestnyi, S. V., & Palchyk, A. M. (2023). Metodyka eksperymentalnoho doslidzhennia rozpodilu intervaliv rukhu v transportnomu pototsi [Methodology for experimental study of headway distribution in traffic flow]. Avtomobilni dorohy i dorozhnie budivnytstvo, 113(2), 61–70. DOI: https://doi.org/10.33744/0365-8171-2023-113.2-061-069 [in Ukrainian].
  14. Shepelev, V., Glushkov, A., Slobodin, I., & Balfaqih, M. (2023). Studying the relationship between the traffic flow structure, the traffic capacity of intersections, and vehicle-related emissions. Mathematics, 11(16), 3591. DOI: https://doi.org/10.3390/math11163591.
  15. Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2015). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865–873. DOI: https://doi.org/10.1109/TITS.2014.2345663.
  16. Zheng, Y., Liu, F., & Hsieh, H. P. (2013). U-Air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1436–1444). DOI: https://doi.org/10.1145/2487575.2487682.