As a pivotal component of intelligent transportation systems (ITS), edge computing nodes significantly enhance system responsiveness and operational efficiency through real-time data processing and intelligent decision-making capabilities. This study systematically investigates the application of edge computing node technology in ITS frameworks, employing a structured analytical approach encompassing four key dimensions: research objectives, methodology, implementation processes, and empirical findings. Through a comprehensive literature review, we synthesize critical technologies, identify practical application scenarios, and project future developmental trajectories of edge computing in intelligent transportation. Our findings demonstrate that edge computing node technology substantially improves transportation system efficiency (by 23-37% in simulation tests) while enhancing safety metrics through reduced latency (≤15ms). The technology's capacity for decentralized real-time data processing effectively addresses urban traffic management challenges, thereby offering robust technical support for developing sustainable smart city transportation ecosystems.

Wanbao Gao, Bo Yang, Bo Zhang, Chao Zhang, Yibo Xu, Chenkai Zhang, Qinxi Lin. Application of Edge Computing Node Technology in Smart Transportation. Distributed Processing System (2025), Vol. 4, Issue 1: 1-8. https://doi.org/10.38007/DPS.2025.040101.

[1]A.J. Dayoub, The Integration of Edge Computing into IoT Application Using AdvantEDGE Platform, Case Study: Mobility, 2025 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), 2025, pp. 1-5.

[2]S. Asaithambi, S. Nallusamy, J. Yang, S. Prajapat, G. Kumar, P.S. Rathore, A secure and trustworthy blockchain-assisted edge computing architecture for industrial internet of things, Scientific Reports 15(1) (2025) 15410.

[3]E. Badidi, O.E. Harrouss, Integrating Edge Computing and Blockchain for Safer and More Efficient Digital Transportation Systems, Procedia Computer Science 251 (2024) 273-280.

[4]J.A. Guerrero-ibanez, S. Zeadally, J. Contreras-Castillo, Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies, IEEE Wireless Communications 22(6) (2015) 122-128.

[5]S. Elkosantini, S. Darmoul, Intelligent Public Transportation Systems: A review of architectures and enabling technologies, 2013 International Conference on Advanced Logistics and Transport, 2013, pp. 233-238.

[6]K. Bilal, O. Khalid, A. Erbad, S.U. Khan, Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers, Computer Networks 130 (2018) 94-120.

[7]M. Dias de Assunц╖цёo, A. da Silva Veith, R. Buyya, Distributed data stream processing and edge computing: A survey on resource elasticity and future directions, Journal of Network and Computer Applications 103 (2018) 1-17.

[8]E. Ahmed, M.H. Rehmani, Mobile Edge Computing: Opportunities, solutions, and challenges, Future Generation Computer Systems 70 (2017) 59-63.

[9]J. Zhao, Z. Zhang, Z. Yi, X. Ma, Q. Zhang, Deployment of edge computing nodes in IoT: Effective implementation of simulated annealing method based on user location, China Communications 21(1) (2024) 279-296.

[10]M. Elassy, M. Al-Hattab, M. Takruri, S. Badawi, Intelligent transportation systems for sustainable smart cities, Transportation Engineering 16 (2024) 100252.

[11]H. Makino, K. Tamada, K. Sakai, S. Kamijo, Solutions for urban traffic issues by ITS technologies, IATSS Research 42(2) (2018) 49-60.

[12]W. Yu, F. Liang, X. He, W.G. Hatcher, C. Lu, J. Lin, X. Yang, A Survey on the Edge Computing for the Internet of Things, IEEE Access 6 (2018) 6900-6919.