Preview

Proceedings of the Southwest State University

Advanced search

Smart Traffic Light Control System Based on Fuzzy Logic

https://doi.org/10.21869/2223-1560-2021-25-4-162-176

Abstract

Purpose of research. Development of a smart traffic light control system based on fuzzy logic with the ability to adjust the time intervals of traffic light signals depending on the traffic situation.

Methods. The definition of input variables for a fuzzy logic control system of smart traffic lights is performed using a vision system. The proposed method of controlling a traffic control device is based on a fuzzy inference system and contains several stages: determination of clear input variables, fuzzification of the values of input variables, aggre-gation of data based on fuzzy rules, defuzzification of values and determination of the delay time of the permitting traffic light signal.

Results. According to the proposed fuzzy model, a device that simulates the operation of a smart traffic light control system was developed. The device is assembled on the basis of the Arduino Uno controller. The developed specialized software model was patented. The number of the certificate of state registration of the computer program "Software for traffic light control based on fuzzy logic" is 2021661796.

Conclusion. The results of experimental studies show the high efficiency of the smart traffic lights in a daily cycle. The program successfully copes with the assessment of the traffic density of cars and pedestrians, adjusting the operating time of traffic lights proportionally. It is proved that the implementation of the developed smart traffic light control system makes it possible to ensure the safety and convenience of road traffic for all road users.

About the Authors

M. V. Bobyr
Southwest State University
Russian Federation

 Maxim V. Bobyr, Dr. of Sci. (Engineering), Professor 

 50 Let Oktyabrya str. 94, Kursk 305040 



N. I. Khrapova
Southwest State University
Russian Federation

 Natalia I. Khrapova, Post-Graduate Student 

50 Let Oktyabrya str. 94, Kursk 305040 



M. A. Lamonov
Southwest State University
Russian Federation

 Maxim A. Lamonov, Post-Graduate Student 

50 Let Oktyabrya str. 94, Kursk 305040 



References

1. Yakovenko N.Y., Yasenok S.N., Nezhelchenko E.V. Upravlenie transportnymi potokami [Traffic flow management]. Belgorod, 2020. 82 p.

2. Bobyr M.V., Milostnaya N.A. Analiz ispol'zovaniya myagkikh arifmeticheskikh operatsii v strukture nechetko-logicheskogo vyvoda [Analysis of the use of soft arithmetic operations in the structure of fuzzy logic inference]. Vestnik komp'yuternykh i informatsionnykh tekhnologii = Bulletin of Computer and Information Technologies, 2015, no. 7 (133), pp. 7-15.

3. Bachmanov M.D. Opyt primeneniya sovremennykh detektorov transporta v zadachakh upravleniya transportnym potokom [The experience of using modern transport detectors in traffic flow control tasks]. Zhurnal elektronnyi. «Avtomobil'. Doroga. Infrastruktura» = Electronic journal. The car. Road. Infrastructure, 2014, no.2(2) December.

4. Evstigneev I.A. Osnovy sozdaniya intellektual'nykh transportnykh sistem na avtomobil'nykh dorogakh federal'nogo znacheniya Rossii [Fundamentals of creating intelligent transport systems on highways of federal significance of Russia]. Moscow, Pero Publ., 2016, 260 p.

5. Bobyr M.V., Kulabukhov S.A. Modelirovanie protsessa upravleniya temperaturnym rezhimom v zone rezaniya na osnove nechetkoi logiki [Modeling of the process of controlling the temperature regime in the cutting zone based on fuzzy logic]. Problemy mashinostroeniya i nadezhnosti mashin = Problems of Mechanical Engineering and Machine Reliability, 2017, no. 3, pp. 76-82/

6. Kiseleva E.A., A Kraeva.A., Savinova Y.S. Obzor nechetkoi logiki v upravlenii [Review of fuzzy logic in management]. Mezhdunarodnyi zhurnal prikladnykh nauk i tekhnologii «Integral» = International Journal of Applied Sciences and Technologies "Integral", 2019, vol. 3.

7. Bobyr M.V., Yakushev A.S., Dorodnykh A. A. Fuzzy devices for cooling the cutting tool of a CNC machine, implemented on FPGA. Measurement: Journal of the International Confederation of Measurements, 2020, 152, 107378. https://doi.org/ 10.1016/j.measurement.2019.107378

8. Bobyr M.V., Emelyanov S.G. Nonlinear method of training neuro-fuzzy models for dynamic control systems. Applied soft computing, 2020, vol. 88, pp. 106030.

9. Chihan Karakuzu, Osman Demirci. Development and hardware implementation of an intelligent traffic light simulator based on fuzzy logic. Applied soft computing 2010, no. 10, pp. 66-73.

10. Ella Bingham, Reinforcement learning in the management of neurofusion traffic signals. European Journal of Operational Research, 2001, no. 131, pp. 232-241.

11. Chou C.-H., Teng J.-S. Fuzzy logic controller for traffic interchange signals. Information Sciences, 2002, no. 143, pp. 73-97.

12. Titov V.S., Bobyr M.V., Milostnaya N.A. ASU prognozirovaniem tochnosti obrabotki detalei [Automated control system predicting the accuracy of machining parts]. Avtomatizatsiya v promyshlennosti = Automation in Industry, 2008, no. 4, pp. 3-4.

13. Uskov A.A. Sistemy s nechetkimi modelyami ob"ektov upravleniya [Systems with fuzzy models of control objects]. Smolensk, SFRUK Publ., 2013. 153 p.

14. Titov V.S., Besedin A.V., Bobyr M.V. Sistema avtomaticheskogo upravleniya sledyashchimi privodami oborudovaniya s ChPU [Automatic control system for tracking drives of CNC equipment]. Patent for utility model RU 27868 U1, 02/20/2003. Application No. 2001129226/20 dated 29.10.2001.

15. Gridin V.N., Titov V.S., Trufanov M.I. Adaptivnye sistemy tekhnicheskogo zreniya [Adaptive systems of technical vision]. Moscow, Nauka Publ., 2009.

16. Bobyr M.V., Kulabukhov S.A. [Mathematical model for a new method of Defazzification in the structure of fuzzy inference]. Mekhatronika, avtomatika i robototekhnika. Sbornik nauchnykh trudov mezhdunarodnoi nauchno-prakticheskoi konferentsii [Mechatronics, automation and robotics. Collection of scientific papers of the international scientific and practical conference]. Novokuznetsk, 2018, pp. 218-220 (In Russ.).

17. Bobyr M.V., Kulabukhov S.A. Defazzifikatsiya vyvoda iz bazy nechetkikh pravil na osnove metoda raznosti ploshchadei [Defazzification of inference from the base of fuzzy rules based on the area difference method]. Vestnik komp'yuternykh i informatsionnykh tekhnologii = Bulletin of Computer and Information Technologies, 2015, no.9, pp.32- 41.

18. Likviyk V.V., Kerre E.E. Defazzification: criteria and classification. The system of fuzzy sets. 1999, no.108, pp. 159-178.

19. Piegat A. Fuzzy modeling and control. Physics-Verlag, Heidelberg, 2001, doi:10.1007/978-3-7908-1824-6

20. Vuong P.T., Madni A.M., Vuong J.B. Implementation of VHDL for fuzzy logic controller. In 2006, the World Congress on Automation, WAC'06. IEEE Computer Society (2006)

21. Arduino.ru : Official website of Arduino in Russia [Electronic resource]. Blank from the screen. Available at: http://arduino.ru /, free.

22. Collotta M., Bello L.L., Po G. A new approach to dynamic traffic light control based on wireless sensor networks and multiple fuzzy logic controllers. Elsevier: Expert Systems with Applications, pp.5403-5415, March 2015

23. Olanrevaju O. M., Obinii A. A., Junaidi S.B. "The concept of fuzzy logic for the interaction of pedestrians with safety-controlled vehicles”. International Journal of Computer Applications (0995-8887), June 2017, vol. 167, no. 1.


Review

For citations:


Bobyr M.V., Khrapova N.I., Lamonov M.A. Smart Traffic Light Control System Based on Fuzzy Logic. Proceedings of the Southwest State University. 2021;25(4):162-174. (In Russ.) https://doi.org/10.21869/2223-1560-2021-25-4-162-176

Views: 434


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


ISSN 2223-1560 (Print)
ISSN 2686-6757 (Online)