TRANSPORT FLOW FORECASTING MODEL BASED ON NEURAL NETWORKS FOR TRAFFIC PREDICTION ON ROADS
Abstract
In connection with the industrialization of modern society, the growth of the transport sys-tems of our country, an increase in certain necessary for the development of the needs of the citi-zens of our country, the number of vehicles of various types continues to increase every year very fast, causing huge traffic jams on transport roads, especially in large cities and megacities. Thus, forecasting traffic flows is an important and necessary component of optimal traffic control in modern conditions of transport network development. As a solution to this problem, this article aims to analyze and describe the application of artificial intelligence methods, in particular neural networks, which represent a modern approach to modeling in complex and nonlinear situations that arise when predicting a traffic flow model. The shown accuracy method is based on the devel-opment of a neural network to predict the daily traffic flow. The expected traffic flow is then com-pared with the actual dataset recorded on the road section and provided by the infrastructure manager. In fact, neural networks are able to learn from past situations and predict future situa-tions on the transport network. In this study, various neural network structures were examined,and the simulation results showed that the best predictions were obtained using the multilayer perceptron architecture, which has a good generalization system with a root mean square error of 0.00927 with the current set of vehicles. The first part of the article is devoted to defining various concepts related to the current research area, including a review of the literature on traffic predic-tion and neural networks. The second part is devoted to describing the problem of traffic conges-tion using forecasting problems and presenting the proposed solution method with an emphasis on artificial neural networks as a means of forecasting demand and its various structures. Then, nu-merical experiments are illustrated by analyzing the forecast results after the formation and test-ing of various neural network architectures.
References
1. Lana I., Del Ser J., Velez M., et Vlahogianni E.I. Road Traffic Forecasting: Recent Advances and New Challenges, IEEE Intell. Transp. Syst. Mag., 2018, Vol. 10, No. 2, pp. 93-109.
2. Zargaryan E.V., Akopdzhanyan Zh.Zh. Issledovanie avtomatizatsii kollaborativnykh robotov i sposoby ikh primeneniya [Research of automation of collaborative robots and methods of their application], Tekhnologii razrabotki informatsionnykh sistem TRIS-2020: Mater. X Mezhdunarodnoy nauchno-tekhnicheskoy konferentsii [Technologies for the development of information systems TRIS-2020. Materials of the X International Scientific and Technical Conference], 2020, pp. 218-223.
3. Zargaryan Yu.A. Zadacha upravlyaemosti v adaptivnoy avtomatnoy obuchaemoy sisteme upravleniya [The problem of controllability in an adaptive automaton learning control system], Tekhnologii razrabotki informatsionnykh sistem TRIS-2020: Mater.X Mezhdunarodnoy nauchno-tekhnicheskoy konferentsii [Technologies for the development of information sys-tems TRIS-2020. Materials of the X International Scientific and Technical Conference], 2020.
4. Zargaryan E.V., Zargaryan Y.A., Dmitrieva I.A., Sakharova O.N. and Pushnina I.V. Modeling design information systems with many criteria. Information Technologies and Engineering – APITECH – 2020, Journal of Physics: Conference Series, 2020, Vol. 2085 (3), pp. 032057(1-7). DOI: 10.1088/1742-6596/1679/3/032057.
5. Kamarianakis Y. et Prastacos P. Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches, Transp. Res. Rec. J. Transp. Res. Board, Janv. 2003, Vol. 1857, No. 1, pp. 74-84.
6. Zargaryan E.V., Zargaryan Y.A., Kapc I.V., Sakharova O.N., Kalyakina I.M and Dmitrieva I.A. Method of estimating the Pareto-optimal solutions based on the usefulness, International Conference on Advances in Material Science and Technology - CAMSTech-2020: IOP Conf. Series: Materials Science and Engineering, 2020, Vol. 919 (2), pp. 022027 (1-8). DOI: 10.1088/1757-899X/919/2/022027.
7. Nagatani T. The physics of traffic jams, Rep. Prog. Phys., Sept. 2002, Vol. 65, No. 9, pp. 1331-1386.
8. Jiber M., Lamouik I., Ali Y., et Sabri M.A. Traffic flow prediction using neural network, in 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, 2018, pp. 1-4.
9. Zhang Y.et Liu Y. Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting, 2009, Vol. 3, No. 3, pp. 7.
10. Beloglazov D., Shapovalov I., Soloviev V., Zargaryan E. The hybrid method of path planning in non-determined environments based on potential fields, ARPN Journal of Engineering and Applied Sciences, 2017, Vol. 12, No. 23, pp. 6762-6772.
11. Zargarjan E.V., Zargarjan Ju.A., Finaev V.I. Information support for the training of fuzzy production account balance in the conditions of incomplete data. Innovative technologies and didactics in teaching (ITDT-2016), Collected papers, 2016, pp. 128-138.
12. Ahmed M.S. et. Cook A.R. Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques, pp. 9.
13. Finaev V.I., Zargaryan Yu.A., Zargaryan E.V., Solov'ev V.V. Formalizatsiya grupp podvizhnykh ob"ektov v usloviyakh neopredelennosti dlya vybora upravlyayushchikh resheniy [Formalization of groups of mobile objects in conditions of uncertainty for the choice of con-trol decisions], Informatizatsiya i svyaz' [Informatization and communication], 2016, No. 3, pp. 56-62.
14. Slimani I., Farissi I.E.l, et Achchab S. Artificial Neural Networks for Demand Forecasting: Application Using Moroccan Supermarket Data, 2015.
15. Slimani I., Farissi I. El, et Achchab S. Configuration and implementation of a daily artificial neural network-based forecasting system using real supermarket data, Int. J. Logist. Syst. Manag., 2017, Vol. 28, No. 2, pp. 144-163.
16. Pushnina I.V. Sistema upravleniya podvizhnym ob"ektom v usloviyakh neopredelennosti [The control system of a moving object in conditions of uncertainty], Nauka i obrazovanie na rubezhe tysyacheletiy: Sb. nauchno-issledovatel'skikh rabot [Science and education at the turn of the millennium: A collection of research papers]. Kislovodsk, 2018, pp. 65-74.
17. Wang X., Wang C. Time series data cleaning: A survey, IEEE Access, 2020, Vol. 8, pp. 1866-1881. DOI: 10.1109/ACCESS.2019.2962152.
18. Data-driven smart cities: Big Data, analytics, and security, 2018. Available at: https://skelia.com/ articles/data-driven-smart-cities-big-data-analytics-and-security/ (accessed 14 september2020).
19. Kim J., Tae D., Seok J. A survey of missing data imputation using generative adversarial net-works, Proc. of the 2020 Int. Conf. on Artificial Intelligence in Information and Communica-tion, ICAIIC 2020, pp. 454-456. DOI: 10.1109/ICAIIC48513.2020.9065044.
20. Dmitrieva I.A., Mileshko L.P., Begun O.V., Berezhnaya A.V. Information Modernization of The General Theory Of Environmental Safety Ensuring, IOP Conference Series: Materials Science and Engineering. III International Scientific Conference. Krasnoyarsk, 2021, pp. 12072.
21. Ivanova N.A., Begun O.V., Dmitrieva I.A., Mileshko L.P., Sklifus R.V. Impact Of Road Transport on The Environmental Situation In The Urban Environment, European Proceedings of Social and Behavioural Sciences EpSBS. Krasnoyarsk, Russia, 2021, pp. 2600-2606.
