隨著人口的增加，城市的交通愈發擁擠，傳統的定時固定交通號誌已經無法滿足道路上複雜的情況，隨著科技的進步，人工智慧應用在交通號誌上，讓城市的交通管理有了新的解決方案，而人工智慧交通號誌不僅帶來交通上的效率，也帶來了相關的效益，除了減少碳排放量，交通數據的收集還可以提供有關事故發生的頻率、類型和嚴重程度的訊息。它們還可以揭示交通事故發生的地點和時間，以幫助識別潛在的危險區域或交通瓶頸。通過這些數據，管理層可以針對特定的路段或區域提出改進和安全措施，以減少事故的發生，提高道路的安全性。
本研究基於計劃行為理論，旨在探討道路參與者在面對人工智慧交通號誌時的相關變數以及行為意圖對交通管理的影響。根據文獻探討，安全性和效率性是交通管理中最為重要的二個要素，也是道路參與者在使用人工智慧交通號誌時顯著的影響其行為意圖的重要變數。
本研究結果，讓管理層更快速的知道使用人工智慧交通號誌時最關切的因素，以及對人工智慧交通號誌的提升與推廣當做借鑑。

With the increasing population, urban traffic has become increasingly congested. Traditional fixed-time traffic signals are no longer sufficient to cope with the complex situations on the roads. With advancements in technology, artificial intelligence has been applied to traffic signals, providing new solutions for urban traffic management. AI traffic signals improve traffic efficiency also bring about various benefits. Apart from reducing carbon emissions, the collection of traffic data can provide information about the frequency, types, and severity of accidents. They can also reveal the locations and times where traffic accidents occur, helping to identify potential danger zones or bottlenecks. With this data, management can propose improvements and safety measures for specific road sections or areas to reduce accidents and enhance road safety.
This study is based on the theory of planned behavior and aims to explore the relevant variables and behavioral intentions of road participants when faced with AI traffic signals and their impact on traffic management. Based on the literature review, safety and efficiency are the two most important factors in traffic management and significantly influence the behavioral intentions of road participants when using AI traffic signals.
The findings of this study allow management to quickly identify the most critical factors when using AI traffic signals and serve as a reference for enhancing and promoting the use of AI-powered traffic signals.

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