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Edge computing and AI-driven intelligent traffic monitoring and optimization
- 1 Electrical and Computer Engineering, University of Illinois Urbana Champaign, Urbana, IL, USA
- 2 Electrical & Computer Engineering, New York University, New York, NY, USA
- 3 Business Intelligence, Engineering School of Information and Digital Technologies, Villejuif, France
- 4 Electrical Engineering, New York University, NY, USA
- 5 Master of Arts in International Economics & International Political Economy, Johns Hopkins University, USA
* Author to whom correspondence should be addressed.
Abstract
Intelligent transportation system is a comprehensive system engineering, involving real-time data processing, security and privacy protection and other challenges. This paper discusses the key role of edge computing in intelligent transportation, especially its combination with SLAM technology. Edge computing enables faster data processing and response times by placing computing and data storage resources near the data source and end users, improving the efficiency and reliability of intelligent transportation systems. At the same time, edge computing can also enhance information security and privacy protection, and bring more possibilities for the future development of intelligent transportation systems. In the future, with the progress and development of technology, we can look forward to the deeper application of edge computing and SLAM technology in the field of intelligent transportation, bringing more innovation and development to smart city transportation.
Keywords
Intelligent Transportation, Edge Computing, SLAM Technology, Data Processing, Security
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Cite this article
Wang,B.;Zheng,H.;Qian,K.;Zhan,X.;Wang,J. (2024). Edge computing and AI-driven intelligent traffic monitoring and optimization. Applied and Computational Engineering,77,225-230.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
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