Managing Transport Systems with Artificial Intelligence

Document Type : Research Paper

Authors

1 Prof., V. N. Karazin Kharkiv National University, Kharkiv 61022, Ukraine, Daugavpils University, Daugavpils, LV – 5401, Latvia, Kharkiv National Automobile and Highway University, Kharkiv, 61002, Ukraine.

2 Prof., Department of Economic Sciences, Buketov Karagandy University, Karaganda, Kazakhstan.

3 Prof., Department of Economic Theories, Entrepreneurship and Trade, Khmelnytskyi National University, Khmelnytskyi, 29016, Ukraine.

4 Associate prof., Faculty of Marine Technologies and Natural Sciences, Klaipeda University, Klaipeda University, 92294. Klaipėda. Lithuania.

5 Associate prof., Department of Economics and Entrepreneurship named after Professor I.M. Bryukhovetskyi, Sumy National Agrarian University, Sumy, 40000, Ukraine.

6 Postgraduate student, Kharkiv National Automobile and Highway University, Kharkiv, 61002, Ukraine.

Abstract

Problems caused by the growth of traffic in cities require modern management approaches to improve the situation with the routing of traffic flow. This article aims to develop a conceptual system that uses computer vision technology to collect and process data from vehicles. It describes the technology of computer vision as an opportunity to improve routing algorithms by processing large data streams that reflect real situations and causes that affect route optimization. The result of using the color vision system is to provide more accurate and timely information to drivers, allowing them to make informed decisions about their routes. This will reduce traffic congestion, improve transport efficiency, and minimize the negative impact on the environment.

Keywords

References

Babenko V.A. (2013). Formation of economic-mathematical model for process dynamics of innovative technologies management at agroindustrial enterprises. Actual Problems of Economics. 139 (1), 182-186. URL: https://www.scopus.com/record/display.uri?eid=2-s2.0-84929991982&origin=inward&txGid=c69f0746cede0da5f287471cd68808af
Babenko, V., Chebanova, N., Ryzhikova, N., Rudenko, S, Birchenko, N. (2018). Research into the process of multi-level management of enterprise production activities with taking risks into consideration. Eastern-European Journal of Enterprise Technologies, 1, 3 (91), 4-12. http://dx.doi.org/10.15587/1729-4061.2018.123461
Can Sağlam, Y. (2022). Smart logistics management in the age of digital transformation: A systematic literature review. Journal of Human and Social Science Researches, 14(4), 4263-4280.
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems (2023). URL: https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321
Fuzzy Hierarchy Analysis Based Microservice Splitting Result Evaluation (2023). URL: https://www.computer.org/csdl/proceedings-article/ipccc/2023/10253880/1RlnPOEVxbq
Gontareva, I., Babenko, V., Shmatko, N., Litvinov, O., Obruch, H. (2020). The Model of Network Consulting Communication at the Early Stages of Entrepreneurship. WSEAS Transactions on Environment and Development, 16(39), 390-396. https://doi.org/10.37394/232015.2020.16.39
Govindan, K., Soleimani, H., & Kannan, D. (2018). A review of definitions and measures of green supply chain management: A framework for future research directions and a case study in the automotive industry. Journal of Cleaner Production, 195, 1237-1251. https://doi.org/10.1016/j.jclepro.2018.05.067
Hrabovskyi, Y., Babenko, V., Al’boschiy, O., Gerasimenko, V. (2020). Development of a Technology for Automation of Work with Sources of Information on the Internet. WSEAS Transactions on Business and Economics, 17 (25), 231-240. https://doi.org/10.37394/23207.2020.17.25
Jagtap, S., & Gupta, S. (2020). Fuzzy multi-criteria decision-making approach for sustainable supplier selection in the logistics sector: A case study from India. Sustainable Production and Consumption, 23, 1-17. https://doi.org/10.1016/j.spc.2020.02.004
Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics (2023). URL: https://www.computer.org/csdl/proceedings-article/ipccc/2023/10253884/1RlnOoMnbr2
Kashchena N., Chmil H., Nesterenko I., Lutsenko O., Kovalevska N. (2024). Diagnostics as a Tool for Managing Behavior and Economic Activity of Retailers in the Conditions of Digital Business Transformation. Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies. 194, 149–173. Springer, Cham. https://doi.org/10.1007/978-3-031-53984-8_7
Khademolqorani, S., & Hamadani, M. (2018). A decision support system for selecting logistics providers based on quality function deployment and fuzzy TOPSIS.Applied Soft Computing, 72, 304-315.
Kuznetsov, A., Kavun, S., Smirnov, O., Babenko, V., Nakisko, O., Kuznetsova, K. (2019). Malware Correlation Monitoring in Computer Networks of Promising Smart Grids. 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings, 8764228, 347-352. doi: 10.1109/ESS.2019.8764228 Available from: https://ieeexplore.ieee.org/document/8879793
Kyrylieva L., Polyvana L., Kashchena N., Naumova T., Akimova N. (2023). Organizational aspects of forming an information and analytical service for the management of trade enterprises in the period of digitalization. Financial and Credit Activity Problems of Theory and Practice, 3(50), 127–138. [in Ukrainian] https://doi.org/10.55643/fcaptp.3.50.2023.3996
Mishra, M. K., A.Muthulakshmi, D., Mishra, D. B. R., & Sasikala, D. M. (2024). Optimizing Decision-Making Process In Supply Chain Management Through Intelligent Systems. Migration Letters, 21(S6), 1107–1113. https://doi.org/10.59670/ml.v21iS6.8164
Modelling the relationship between travel behaviours and social disadvantage (2016). URL: https://www.sciencedirect.com/science/article/abs/pii/S0965856416000082
Nesterenko I., Kashchena N., Chmil H., Chumak O., Shtyk Yu., Nesterenko O., Kovalevska N. (2024). Devising a methodological approach to identifying the economic potential of production costs for eco-innovative products. Eastern-European Journal of Enterprise Technologies, 3, 13 (129), 6–15. doi: 10.15587/1729-4061.2024.304805
On-Line Network Traffic Anomaly Detection Based on Tensor Sketch (2023). URL: https://www.computer.org/csdl/journal/td/2023/12/10255295/1Qzyzg8qcCI
Peťo, I., & Peťo, M. (2013). The decision-making systems model for logistics. In Proceedings of the International Conference on Logistics and Supply Chain Management, 1-8. https://doi.org/10.1007/s10479-018-2904-0
Perotti, S., & Yıldız Çankaya, E.(2022). Drivers and inhibitors of smart logistics: A systematic literature review.Journal of Human and Social Science Researches, 13(1), 11-31.Pylypenko, A.A., Savytska, N.L., Vaksman, R.V., Uhodnikova, O.I., Schevchenko, V.S. (2019). Methodical maintenance of management of logistic activity of the trade enterprise: Economic and legal support. Journal of Advanced Research in Law and Economics, 10(6), 1723–1731 URL: https://journals.aserspublishing.eu/jarle/article/view/4943.
Real-Time Traffic Control and Safety Measures Analysis Using LiDAR Sensor during Traffic Signal Failures (2023). URL: https://www.researchgate.net/publication/374926696_Real-Time_Traffic_Control_and_Safety_Measures_Analysis_Using_LiDAR_Sensor_during_Traffic_Signal_Failures
Savytska, N., Babenko, V., Chmil, H., Priadko, O., & Bubenets, I. (2023). Digitalization of Business Development Marketing Tools in the B2C Market. Journal of Information Technology Management, 15(1), 124-134. https://doi.org/ 10.22059/jitm.2023.90740
Savytska N.; Zhehus O.; Polevych K.; Prydko O. & Bubenets I. (2024). Enterprise Resilience Behavioral Management in a Decision Support System. Journal of Information Technology Management, 16 (4), 100-121. https://doi.org/10.22059/jitm.2024.99053
Shtal, T., Proskurnina, N., Savytska, N., Mykhailova, M., Bubenets, I. (2023). Analysis of the Vectors of Digital Transformation of Retail Trade in Ukraine: Determination Methodology and Trends. Economic Affairs, 68(Special Issue), 939-945. DOI: 10.46852/0424-2513.2s.2023.42
Traffic Management System and Traffic Light Control in Smart City to Reduce Traffic Congestion (2023). URL: https://www.researchgate.net/publication/372768522_Traffic_Management_system_and_Traffic_Light_Control_in_Smart_City_to_Reduce_Traffic_Congestion
Zoubek, J., & Šimon, J. (2021). Enhancing decision-making processes in smart logistics through big data analytics: A case study approach. Logistics, 5(3), 34. https://doi.org/10.3390/logistics5030034
Windt, K., & Hulsmann, M. (2007). The impact of information technology on decision-making processes in logistics: A systematic literature review and future research directions.International Journal of Logistics Research and Applications, 10(3), 227-244.
Yan, X., et al. (2022). Smart logistics: Driving factors and barriers to adoption—a systematic literature review approach.Logistics, 6(2), 21.
 
 
 
 
 
Journal of Information Technology Management
Volume 17, Issue 2
2025
Pages 13-27

Files

Share

How to cite

Statistics