在全球各大城市中，交通堵塞問題日漸嚴重，也給全球城市帶來了巨大的影響和壓力。交通堵塞將導致二氧化碳排放量增加、燃料消耗劇增以及高峰時段的交通旅行時間變長等諸多社會、經濟和環境問題。為解決這一問題，本博士論文提出了幾個創新之多方向性策略，充分運用且成功融合了交通流之預測、強化學習預測模型、機器學習技術、高級駕駛員輔助系統（ADAS）和車載隨機網絡（VANET）的優勢，以最大化地提高道路利用率並找出最有效的重新路由路徑。

在本博士論文中的幾個策略分別是基於實時交通信息和預測的未來車輛流量，優化路徑選擇進一步增加交通重新路由的準確度與效能。我們在所設計的演算法中將根據車輛之目的地、道路密度和當前交通量通盤考量，進而將最佳化之路徑做為選擇，並藉助動態時間更新演算法，提供了最有效的重新路由方案，極大地幫助用路人選擇最有效的重新路由路徑，從而顯著改善了交通狀況。

另外，我們亦提出基於強化學習的創新獎勵機制，並採用長短期記憶（LSTM）的預測模型來進行改善紅綠燈時向與時長之控制。 此預測模型準確預測未來的道路密度、車速和交通量，並加入創新的獎勵函數，使得我們在動態紅綠燈時長的控制上有卓越的成效。而這種方法確保決策符合環境上之需求並隨時調整，從而提供全面的交通管理解決方案。

通過在 SUMO (Simulation of Urban Mobility) 上進行模擬，我們所提出之幾個策略皆在交通旅行時間、二氧化碳排放量、燃料消耗方面都優於現有文獻中的各種策略，模擬之結果也驗證所提出之幾個策略有顯著地提高交通之效率並減少對環境的影響。毫無疑問地，本博士論文所提出的幾個增強、全面的策略對於解決交通堵塞問題做出了重大貢獻。

The problem of traffic congestion is becoming increasingly severe, bringing substantial impact and stress to global urban environments. Traffic congestion results in increased carbon dioxide emission, escalated fuel consumption, and elongated travel time during peak hours. All of which pose numerous social, economic, and environmental challenges. To address these issues, this dissertation proposes several innovative and multifaceted strategies. These strategies successfully incorporate and integrate traffic flow prediction, reinforcement learning mechanism, machine learning technologies, advanced driver-assistance systems (ADAS), and the advantages of the vehicular ad-hoc network (VANET) with the ultimate goal of maximizing road utilization and identifying the most efficient rerouting paths.

The proposed strategies in this dissertation are designed according to the real-time traffic information and predicted future vehicle flow to optimize path selection and further enhance the accuracy and efficiency of traffic rerouting. These strategies consider the destination of vehicles, road density, and current traffic volume to make comprehensive routing decisions. Furthermore, the dynamic time updating algorithm is incorporated to provide the most efficient rerouting plans, significantly aiding road users in choosing the most effective rerouting paths and substantially improving traffic conditions.

In addition, the strategies proposed in this dissertation utilize a novel reward mechanism in reinforcement learning with a long short-term memory (LSTM) prediction model to improve the control of traffic signal timing and duration. This prediction model accurately forecasts future road density, vehicle speed, and traffic volume and incorporates an innovative reward function, leading to exceptional performance in dynamic traffic light timing control. This proposed method ensures that decisions align with environmental needs and can be adjusted in real-time, thereby providing a comprehensive traffic management solution.

Through simulations conducted on the simulation of urban mobility (SUMO) platform, the proposed strategies in this dissertation outperform those in the literature in terms of travel time, carbon dioxide emissions, and fuel consumption. The simulation results unquestionably demonstrate that these strategies significantly enhance traffic efficiency and mitigate environmental impact. Undoubtedly, the enhanced and comprehensive strategies proposed in this dissertation make significant contributions to solving the problem of traffic congestion.

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