通過整合來自各種感測器和攝影機的數據，數位孿生（Digital Twin, DT）能夠創建一個詳細的現實世界道路交通和用路人（Road User, RU）的虛擬表示（virtual representation），增強了各種交通應用的易懂程度和決策方法。然而，現代交通環境存在一個挑戰，即需要用路人配備感測器來建立準確的虛擬表示。沒有這些感測器，則難以在DT中為用路人建立虛擬表示，從而限制了DT系統的有效性。因此，解決這一差距對於DT技術在交通管理中的成功部署至關重要。
在本論文中，我們首先設計了一個基於DT的系統，旨在使用行動邊緣運算（MEC）伺服器配對和更新道路交通環境中的RU資訊。我們的系統設計目的是支援配備感測器和未配備感測器的RU，確保全面的覆蓋和服務可及性。為了實現這一目標，我們的系統包括兩個負責更新RU資訊的模組: 1）定位模組（Localization module）負責更新RU自身的位置資訊，以及 2）用路人偵測模組（Road User detection module）負責偵測交通環境中的其他RU，收集周圍RU的數據並在DT中更新他們的虛擬表示。
在系統設計之後，我們提出了一個新的最佳化問題，目標是最小化Age of Incorrect Information（AoII）指標。該指標衡量DT內虛擬表示的新鮮度和準確度。為了解決此問題，我們提出了一種名為 AoII minimization by Update Selection in Digital Twin（AoII-USDT）的演算法，用於決定每個RU的更新策略，確保資訊保持盡可能的及時和準確。
我們還探討了我們的系統在多MEC場景中的應用，展示了我們的系統和演算法的擴展性和適用性。模擬結果顯示，AoII-USDT無論是在單一還是多MEC場景中，在整體AoII、新鮮度改進和準確性方面顯著優於其他先進的算法。這些結果突顯了我們方法的有效性及其在實際應用中提高交通安全的潛力。

By integrating data from various sensors and cameras, a Digital Twin (DT) creates a detailed virtual representation of real-world road traffic and road users (RUs). This integration enhances understanding and decision-making processes for various traffic applications.
However, modern traffic environments pose a challenge as they require sensors on RUs to create an accurate virtual representation. Without these sensors, it is difficult to create virtual representations for RUs in the DT, limiting the system's effectiveness. Therefore, addressing this gap is crucial for the successful deployment of DT technology in traffic management.
In this thesis, we first design a DT-based system aimed at pairing and updating the information of RUs within road traffic environments using a Mobile Edge Computing (MEC) server. Our system is designed to support both RUs equipped with sensors and those without, ensuring comprehensive coverage and service effectiveness. To achieve this, our system includes two distinct modules responsible for updating the information of RUs: 1) Localization module for updating the location of the RU itself, and 2) Road User detection module to detect the other RUs for updating.
Following the system design, we formulate a new optimization problem aimed at minimizing the Age of Incorrect Information (AoII) metric. This metric measures the freshness and accuracy of the information within the DT. To address this optimization problem, we propose an algorithm named AoII minimization by Update Selection in Digital Twin (AoII-USDT). This algorithm determines the updating policy for each RU, ensuring that the information remains as current and accurate as possible.
We also explore the application of our system within a multi-MEC scenario, demonstrating the extensibility and adaptability of our system and algorithm.
Simulation results show that AoII-USDT significantly outperforms state-of-the-art algorithms in terms of total AoII, freshness improvement, and accuracy across both single and multi-MEC scenarios. These results highlight the effectiveness of our approach and its potential to enhance traffic safety in real-world applications.

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