Large Language Model-Powered Digital Traffic Engineers: The Framework and Case Studies

This paper presents a novel Digital Traffic Engineers (DTEs) framework, leveraging Large Language Models (LLMs) to intelligently interpret human language and automate the creation of traffic control strategies. This advancement eliminates the need for manual scheme creation, reducing the workload of...

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Veröffentlicht in:	IEEE journal of radio frequency identification (Online) 2024, Vol.8, p.780-787
Hauptverfasser:	Dai, Xingyuan, Tang, Yiqing, Chen, Yuanyuan, Zhang, Xiqiao, Lv, Yisheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive control digital traffic engineers Engineers intelligent transportation systems Knowledge engineering Large language models Planning Radiofrequency identification Task analysis Timing Traffic control traffic control and management Traffic management Transportation Urban transportation
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