With the urbanization process and the continuous growth of traffic demand, intelligent transportation system plays an important role in solving traffic problems. As the core technology of artificial intelligence, machine learning can improve the operation efficiency of traffic system, reduce traffic congestion and reduce the incidence of traffic accidents. This paper summarizes the application and optimization of machine learning in intelligent transportation system, and puts forward the methods and steps of utility evaluation and optimization effect evaluation. By analyzing research conclusions and lessons learned, the paper emphasizes the importance of data quality and scale, algorithm selection, model interpretability and robustness, and interdisciplinary collaboration and comprehensive optimization. However, this paper also points out some shortcomings, such as data quality, algorithm interpretability and privacy protection, which need to be further studied and discussed.

Yufei Tao, Inti Ruminahui Toalombo Chicaiz. Utility Evaluation and Optimization of Machine Learning in Intelligent Transportation Systems. International Journal of Frontiers in Engineering Technology (2024), Vol. 6, Issue 3: 33-39. https://doi.org/10.25236/IJFET.2024.060305.

[1] J P, L M, N M, et al. A vehicular network based intelligent transport system for smart cities using machine learning algorithms. Scientific reports, 2024, 14(1): 468-469.

[2] Ndam A N, Wangui A W, Adamou A A, et al. A Machine Learning Scheme for Speed Prediction in Intelligent Transportation Systems Using a Bi-LSTM Based Model. International Journal of Engineering Research in Africa, 2023, 70, 207-233.

[3] I. A S, V. T M, G. I E. Selection of Potential Regions for the Creation of Intelligent Transportation Systems Based on the Machine Learning Algorithm Random Forest. Applied Sciences, 2023, 13(6): 4024-4025.

[4] Alghamdi S A, Imran T, Mursi T K, et al. A Vehicle Classification System for Intelligent Transport System using Machine Learning in Constrained Environment. International Journal of Advanced Computer Science and Applications, 2023, 14(7):15-19

[5] K. S A, S. R, M. R, et al. Intellectual transport system for human safety using machine learning approach. Measurement: Sensors, 2022, 24-26

[6] Issam D, K. S K A, Tarek N, et al. Intelligent transportation systems: A survey on modern hardware devices for the era of machine learning. Journal of King Saud University - Computer and Information Sciences, 2022, 34(8PB): 5921-5942.

[7] Muhammad S, Sagheer A, et al. Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal, 2022, 23(3): 417-426.

[8] Khan Hameed Kushwah. Machine learning driven intelligent and self-adaptive system for traffic management in smart cities. Computing, 2022, 104(5): 1-15.

[9] Tingting Y, Wilson N R, Esteve C R, et al. Machine learning for next‐generation intelligent transportation systems: A survey. Transactions on Emerging Telecommunications Technologies, 2021, 33(4):59-60 

[10] Jingyao W, Ranjan M P, Nallappan G. Machine learning-based human-robot interaction in ITS. Information Processing and Management, 2022, 59(1):69-75

[11] Zhihan Lv, Yuxi Li, Hailin Feng, Haibin Lv. Deep learning for Security in Digital Twins of Cooperative Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems, 2021.