Main Article Content


The article presents the initial experience (spring-summer 2023) of using artificial neural networks (ANN) to improve traffic management in the large Russian city of Tyumen. Using the example of one of the intersections of the city's road network, it is shown how much transport delays are reduced when the duration of the traffic light cycle phases is quickly adjusted to the actual traffic intensity when compared with the usual previously used traffic light predictive mode. For the specific intersection of Odesskaya and Kotovskogo streets in Tyumen, considered in this article, the traffic light control mode using an ANN can significantly (by 20.6 ... 22.4%) reduce the average delay time of vehicles. It is also important that the reduction in traffic delays, which is possible with the regulation of traffic using ANN, helps to reduce stress for road users and improve road safety. The article presents historical data illustrating the dynamics of changes in the field of traffic management and road safety in Tyumen. This information confirms the thesis about the dialectic of systemic development and the need for a gradual increase in the intellectual component of traffic management in large cities. The Applications (Appendix A and Appendix B) present the code of the auxiliary procedures and functions module and the code of the main data collection module used to optimize the traffic light control mode at the experimental intersection of the Tyumen road network. The main conclusion of the study is that the use of an ANN allows for taking into account a much larger number of factors and optimizing the control of the entire object, consisting of several intersections, which is not achievable using predictive modes and local adaptive control.


Management of road traffic Traffic light control Artificial neural networks Safety of road traffic City traffic efficiency Tyumen Russia

Article Details


  1. C. McShane, “The origins and globalization of traffic control signals,” Journal of Urban history, vol. 25, no. 3, pp. 379–404, 1999, doi: 10.1177/009614429902500304.
  2. M. Y. Blinkin and E. M. Reshetova, Road Safety: The History of the Issue, International Experience, Basic Institutions [In Russian]. Moscow, Russia: House of the Higher School of Economics, 2013.
  3. K. N. Qureshi and A. H. Abdullah, “A survey on intelligent transportation systems,” Middle-East Journal of Scientific Research, vol. 15, no. 5, pp. 629–642, 2013, doi: 10.5829/idosi.mejsr.2013.15.5.11215.
  4. A. Abunei, C.-R. Comşa, and I. Bogdan, “Implementation of ETSI ITS-G5 based inter-vehicle communication embedded system,” in 2017 International Symposium on Signals, Circuits and Systems (ISSCS), 2017, pp. 1–4, doi: 10.1109/isscs.2017.8034921.
  5. Russian Federal State Statistics Service, “Population of the Russian Federation by municipality [In Russian],”, 2023. (accessed May 01, 2023).
  6. A. Petrov and V. Kolesov, “Entropic analysis of dynamics of road safety system organization in the largest Russian cities,” in IOP Conference series: earth and environmental science, 2018, vol. 177, p. 12015, doi: 10.1088/1755-1315/177/1/012015.
  7. C. Chen, B. Liu, S. Wan, P. Qiao, and Q. Pei, “An edge traffic flow detection scheme based on deep learning in an intelligent transportation system,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1840–1852, 2020, doi: 10.1109/TITS.2020.3025687.
  8. M. Ahmed, S. Masood, M. Ahmad, and A. A. Abd El-Latif, “Intelligent driver drowsiness detection for traffic safety based on multi CNN deep model and facial subsampling,” IEEE transactions on intelligent transportation systems, vol. 23, no. 10, pp. 19743–19752, 2021, doi: 10.1109/TITS.2021.3134222.
  9. A. Boukerche and J. Wang, “A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model,” Ad Hoc Networks, vol. 106, p. 102224, 2020, doi: 10.1016/j.adhoc.2020.102224.
  10. G. Meena, D. Sharma, and M. Mahrishi, “Traffic prediction for intelligent transportation system using machine learning,” in 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), 2020, pp. 145–148, doi: 10.1109/ICETCE48199.2020.9091758.
  11. H. Yuan and G. Li, “A survey of traffic prediction: from spatio-temporal data to intelligent transportation,” Data Science and Engineering, vol. 6, pp. 63–85, 2021, doi: 10.1007/s41019-020-00151-z.
  12. J. Wang, M. R. Pradhan, and N. Gunasekaran, “Machine learning-based human-robot interaction in ITS,” Information Processing & Management, vol. 59, no. 1, p. 102750, 2022, doi: 10.1016/j.ipm.2021.102750.
  13. Y. Li and C. Shahabi, “A brief overview of machine learning methods for short-term traffic forecasting and future directions,” Sigspatial Special, vol. 10, no. 1, pp. 3–9, 2018, doi: 10.1145/3231541.3231544.
  14. A. Boukerche and J. Wang, “Machine learning-based traffic prediction models for intelligent transportation systems,” Computer Networks, vol. 181, p. 107530, 2020, doi: 10.1016/j.comnet.2020.107484.
  15. A. Boukerche, Y. Tao, and P. Sun, “Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems,” Computer networks, vol. 182, p. 107484, 2020, doi: 10.1016/j.comnet.2020.107484.
  16. M. M. Ahsan, M. A. P. Mahmud, P. K. Saha, K. D. Gupta, and Z. Siddique, “Effect of data scaling methods on machine learning algorithms and model performance,” Technologies, vol. 9, no. 3, p. 52, 2021, doi: 10.3390/technologies9030052.
  17. S. George and A. K. Santra, “Traffic prediction using multifaceted techniques: a survey,” Wireless Personal Communications, vol. 115, pp. 1047–1106, 2020, doi: 10.1007/s11277-020-07612-8.
  18. D. Gangwani and P. Gangwani, “Applications of machine learning and artificial intelligence in intelligent transportation system: A review,” Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2020, pp. 203–216, 2021, doi: 10.1007/978-981-16-3067-5_16.
  19. F. Zantalis, G. Koulouras, S. Karabetsos, and D. Kandris, “A review of machine learning and IoT in smart transportation,” Future Internet, vol. 11, no. 4, p. 94, 2019, doi: 10.3390/FI11040094.
  20. N. Silva, V. Shah, J. Soares, and H. Rodrigues, “Road anomalies detection system evaluation,” Sensors, vol. 18, no. 7, p. 1984, 2018, doi: 10.3390/s18071984.
  21. A. Yadav, V. More, N. Shinde, M. Nerurkar, and N. Sakhare, “Adaptive traffic management system using IoT and machine learning,” Int. J. Sci. Res. Sci. Eng. Technol, vol. 6, pp. 216–229, 2019, doi: 10.32628/IJSRSET196146.
  22. N. Sakhare et al., “Image processing and IoT based dynamic traffic management system,” International Journal of Scientific Research in Science, Engineering and Technology, 2020, doi: 10.32628/IJSRSET207230.
  23. M. Stojmenovic, “Real time machine learning based car detection in images with fast training,” Machine Vision and Applications, vol. 17, no. 3, pp. 163–172, 2006, doi: 10.1007/s00138-006-0022-638.
  24. S. Omar, A. Ngadi, and H. H. Jebur, “Machine learning techniques for anomaly detection: an overview,” International Journal of Computer Applications, vol. 79, no. 2, 2013, doi: 10.5120/13715-147810.
  25. A. Kama, M. Diallo, M. S. Drame, M. L. Ndiaye, A. Ndiaye, and P. A. Ndiaye, “Monitoring the performance of solar street lights in Sahelian environment: case study of Senegal,” in 2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017, pp. 56–61, doi: 10.1109/DeSE.2017.43.
  26. A. K. Tripathy, A. K. Mishra, and T. K. Das, “Smart lighting: Intelligent and weather adaptive lighting in street lights using IOT,” in 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2017, pp. 1236–1239, doi: 10.1109/ICICICT1.2017.834274621.
  27. G. Jia, G. Han, A. Li, and J. Du, “SSL: Smart street lamp based on fog computing for smarter cities,” IEEE Transactions on Industrial Informatics, vol. 14, no. 11, pp. 4995–5004, 2018, doi: 10.1109/TII.2018.285791820.
  28. P. Mohandas, J. S. A. Dhanaraj, and X.-Z. Gao, “Artificial neural network based smart and energy efficient street lighting system: A case study for residential area in Hosur,” Sustainable Cities and Society, vol. 48, p. 101499, 2019, doi: 10.1016/j.scs.2019.10149919.
  29. Z. Li, H. Yu, G. Zhang, S. Dong, and C.-Z. Xu, “Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning,” Transportation Research Part C: Emerging Technologies, vol. 125, p. 103059, 2021, doi: 10.1016/j.trc.2021.103059.
  30. D. Srinivasan, M. C. Choy, and R. L. Cheu, “Neural networks for real-time traffic signal control,” IEEE Transactions on intelligent transportation systems, vol. 7, no. 3, pp. 261–272, 2006, doi: 10.1109/TITS.2006.874716.
  31. Z. Zhang, J. Qian, C. Fang, G. Liu, and Q. Su, “Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1–13, 2021, doi: 10.1155/2021/6693636.
  32. Schlothauer & Wauer GmbH, “Software package Lisa+,”, 2023. (accessed Jun. 14, 2023).
  33. PTV Group, “Software package PTV Vissim 11,”, 2023. (accessed Jul. 16, 2023).
  34. Tensor Flow, “Keras: The high-level API for TensorFlow,”, 2023. (accessed Jul. 17, 2023).
  35. A. Petrov and D. Petrova, “Atmospheric pollution in cities of Russia: statistics, causes and characteristics,” in IOP Conference Series: Earth and Environmental Science, 2017, vol. 72, no. 1, p. 12007, doi: 10.1088/1755-1315/72/1/012007.
  36. Federal State Statistics Service of the Russian Federation, “Preliminary estimate of the permanent population of Russian cities [In Russian],”, 2023. (accessed Jun. 28, 2023).
  37. Tyumen City Transport, “History of public transport in Tyumen [In Russian],” Tyumengortrans, 2023. (accessed Jul. 05, 2023).
  38. A. I. Petrov and V. I. Kolesov, “Road traffic accident rate in Russia: Main socio-economic factors of its formation and spatio-temporal features,” Ekonomicheskie i Sotsialnye Peremeny, vol. 14, no. 1, pp. 199–220, 2021, doi: 10.15838/esc.2021.1.73.14.
  39. R. J. Smeed, “Some statistical aspects of road safety research,” Journal of the Royal Statistical Society. Series A (General), vol. 112, no. 1, pp. 1–34, 1949.
  40. A. Megías-Robles, M. T. Sánchez-López, and P. Fernandez-Berrocal, “The relationship between self-reported ability emotional intelligence and risky driving behaviour: Consequences for accident and traffic ticket rate,” Accident Analysis & Prevention, vol. 174, p. 106760, 2022, doi: 10.1016/j.aap.2022.106760.
  41. M. A. Brackett, S. E. Rivers, S. Shiffman, N. Lerner, and P. Salovey, “Relating emotional abilities to social functioning: a comparison of self-report and performance measures of emotional intelligence.,” Journal of personality and social psychology, vol. 91, no. 4, p. 780, 2006, doi: 10.1037/0022-3514.91.4.78.
  42. I. Malygin, V. Komashinsky, and V. V Tsyganov, “International experience and multimodal intelligent transportation system of Russia,” in 2017 Tenth International Conference Management of Large-Scale System Development (MLSD), 2017, pp. 1–5, doi: 10.1109/MLSD.2017.8109658.
  43. A. Asaul, I. Malygin, and V. Komashinskiy, “The project of intellectual multimodal transport system,” Transportation research procedia, vol. 20, pp. 25–30, 2017, doi: 10.1016/j.trpro.2017.01.006.
  44. S. Biswas and I. Ghosh, “Modeling of the drivers’ decision-making behavior during yellow phase,” KSCE Journal of Civil Engineering, vol. 22, pp. 4602–4614, 2018, doi: 10.1007/s12205-018-0666-6.
  45. S. Biswas and I. Ghosh, “Reliability modelling on drivers’ decision during the yellow phase of a signal intersection,” Current Science, vol. 118, no. 4, 2020, doi: 10.18520/cs/v118/i4/654-661.
  46. V. Kolesov and A. Petrov, “Entropy and risks in regional road traffic safety systems,” Transportation research procedia, vol. 50, pp. 262–272, 2020.