Automation of the coordinated road traffic control process

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Authors:


D.Orazbayeva, orcid.org/0000-0001-7987-3899, Academy of Logistics and Transport, Almaty, the Republic of Kazakhstan, -mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.Abzhapbarova, orcid.org/0000-0001-7013-0909, Civil Aviation Academy, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D.Agabekova, orcid.org/0000-0003-1775-2084, L.B.Goncharov Kazakh Automobile and Road Academy, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.Bublikov, orcid.org/0000-0003-3015-6754, Dnipro University of Technology, Dnipro, Ukraine, email: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Taran, orcid.org/0000-0002-3679-2519, Dnipro University of Technology, Dnipro, Ukraine, email: This email address is being protected from spambots. You need JavaScript enabled to view it.


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (1): 158 - 162

https://doi.org/10.33271/nvngu/2022-1/158



Abstract:



Purpose.
To increase output of traffic network while deriving new dependencies of transport flow characteristics upon control law parameters and using them to develop an algorithm to automate a process of the transport flow coordinated regulation within towns and cities.


Methodology.
The aggregated simulation model, describing processes of road traffic formation within a local section of urban traffic system, is used for the analysis of dependencies to assess quality of transport flow control upon correcting conditions being parameters of traffic cycles within intersections. The abovementioned should involve a PID controller as well as a negative feedback idea to automate control of the road traffic process. In this context, control criterion is applied to determine critical flow intensity if a traffic jam occurs. The criterion makes it possible to identify changes in the road traffic nature. To determine the optimal settings of the PID controller for different stocks of the stability control system, the standard deviation of the controlled variable from the set value and the mean static control error are defined.

The derived dependencies of control quality criteria upon a law of flow regulation have helped develop a new algorithm of the coordinated automated road traffic control within a local section of urban traffic section using standard control action. Simulation experiments made it possible to assess efficiency of the proposed algorithm of control compared with available ones applied in terms of different road conditions.


Findings.
As a part of the studies, algorithm of the coordinated automated road traffic has been developed. It helps support maximum output of road systems while monitoring changes in the traffic environment characteristics.


Originality.
For the first time, dependencies of assessment criteria of traffic flow control upon the parameters of a law of flow intensity regulation have been identified if maximum output of road systems is provided. The dependencies have helped substantiate optimum control criteria for different road conditions while automating a process of coordinated traffic flow regulation within local section of urban traffic network.


Practical value.
The proposed dependencies of assessment criteria of traffic flow control upon the parameters of a law of flow intensity regulation as well as the algorithm of automated coordinated control are the theoretical foundations to solve such an important applied scientific problem as automation of a process of urban traffic flow regulation.



Keywords:
traffic flows, coordinated control, simulation modeling

References:


1. Turpak, S., Trushevsky, V., Kuzkin, O., Gritcay, S., & Taran, I. (2021). Improving the efficiency of vehicle operation and its environmental friendliness within the controlled crossings. Transport problems, 16(3), 119-130. https://doi.org/10.21307/tp-2021-046.

2. Taran, I., & Litvin, V. (2018). Determination of rational parameters for urban bus route with combined operating mode. Transport Problems, 13(4), 157-171. https://doi.org/10.20858/tp.2018.13.4.14.

3. Michailidis, I.T., Manolis, D., Michailidis, P., Diakaki, C., & Kosmatopoulos, E.B. (2020). A decentralized optimization approach employing cooperative cycle-regulation in an intersection-centric manner: A complex urban simulative case study. Transportation Research Interdisciplinary Perspectives, 8, 100232. https://doi.org/10.1016/j.trip.2020.100232.

4. Solecka, K., Dumanowski, ., Taran, I., & Litvinova, Y. (2021). Application of the Hierarchy Analysis Method to Assess Interchanges in Cracow. Sustainability, 13, 10593. https://doi.org/10.3390/su131910593.

5. Kapitanov, V., Silyanov, V., Monina, O., & Chubukov, A. (2018). Methods for traffic management efficiency improvement in cities. Transportation Research Procedia, 36, 252-259. https://doi.org/10.1016/j.trpro.2018.12.077.

6. Fanitabasi, F., Gaere, E., & Pournaras, E. (2020). A self-integration testbed for decentralized socio-technical systems. Future Generation Computer Systems, 113, 541-555. https://doi.org/10.1016/j.future.2020.07.036.

7. Agafonov, A., & Myasnikov, V. (2017). Efficiency comparison of the routing algorithms used in centralized traffic management systems. Procedia Engineering, 201, 365-270. https://doi.org/10.1016/j.proeng.2017.09.617.

8. Grner, S., & Vollrath, M. (2021). Challenges for obtaining a system-optimal traffic distribution by giving route advice due to the biased memory of congestion. Transportation Research Procedia, 52, 396-403. https://doi.org/10.1016/j.trpro.2021.01.046.

9. Zhu, Zh., Adouane, L., & Quilliot, A. (2021). A Decentralized Multi-Criteria Optimization Algorithm for Multi-Unmanned Ground Vehicles (MUGVs) Navigation at Signal-Free Intersection. IFAC-PapersOnLine, 54(2), 327-334. https://doi.org/10.1016/j.ifacol.2021.06.038.

10. Maske, H., Chu, T., & Kalabi, U. (2020). Control of traffic light timing using decentralized deep reinforcement learning. IFAC-PapersOnLine, 53(2), 14936-14941. https://doi.org/10.1016/j.ifacol.2020.12.1980.

11. Outay, F., Mengash, H.A., & Adnan, M. (2020). Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges. Transportation Research Part A: Policy and Practice, 141, 116-129. https://doi.org/10.1016/j.tra.2020.09.018.

12. Papamichail, I., Bekiaris-Liberis, N., Delis, A. I., Manolis, D., Mountakis, K.S., Nikolos, I.K., , & Papageorgiou, M. (2019). Motorway traffic flow modelling, estimation and control with vehicle automation and communication systems. Annual Reviews in Control, 48, 325-346. https://doi.org/10.1016/j.arcontrol.2019.09.002.

13. Amer, H.M., Al-Kashoash, H., Hawes, M., Chaqfeh, M., Kemp,A., & Mihaylova, L. (2019). Centralized simulated annealing for alleviating vehicular congestion in smart cities. Technological Forecasting and Social Change, 142, 235-248. https://doi.org/10.1016/j.techfore.2018.09.013.

14. Chow, A.H.F., Sha, R., & Li, S. (2020). Centralised and decentralised signal timing optimisation approaches for network traffic control. Transportation Research Part C: Emerging Technologies, 113, 108-123. https://doi.org/10.1016/j.trc.2019.05.007.

15. Su, Z.C., Chow, Andy H.F., & Zhong, R.X. (2021). Adaptive network traffic control with an integrated model-based and data-driven approach and a decentralised solution method. Transportation Research Part C: Emerging Technologies, 128, 103154. https://doi.org/10.1016/j.trc.2021.103154.

16. Bohnsack, R., Kurtz, H., & Hanelt, A. (2021). Re-examining path dependence in the digital age: The evolution of connected car business models. Research Policy, 50(9), 104328. https://doi.org/10.1016/j.respol.2021.104328.

17. Nguyen, T.H., & Jung, Jason J. (2021). Ant colony optimization-based traffic routing with intersection negotiation for connected vehicles. Applied Soft Computing, 112, 107828. https://doi.org/10.1016/j.asoc.2021.107828.

18. Yao, H., & Li, X. (2020). Decentralized control of connected automated vehicle trajectories in mixed traffic at an isolated signalized intersection. Transportation Research Part C: Emerging Technologies, 121, 102846. https://doi.org/10.1016/j.trc.2020.102846.

19. Wang, C., Peeta, S., & Wang, J. (2021). Incentive-based decentralized routing for connected and autonomous vehicles using information propagation. Transportation Research Part B: Methodological, 149, 138-161. https://doi.org/10.1016/j.trb.2021.05.004.

20. Sun, X., & Yin, Y. (2021). Decentralized game-theoretical approaches for behaviorally-stable and efficient vehicle platooning. Transportation Research Part B: Methodological, 153, 45-69. https://doi.org/10.1016/j.trb.2021.08.012.

21. Bublikov, ., Taran, I., & Tokar, L. (2016). Power Engineering and Information Technologies in Technical Objects Control. Leiden: CRC Press/Balkema Book. Power Engineering and Information Technologies in Technical Objects Control/Annual Proceedings, 249-261.

 

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ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

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