针对既有交通系统缺乏记忆和学习能力、在自主运行发展过程中难以实现自我优化和闭环演进等问题，该文首先提出了一种面向自主式交通系统的数字孪生系统设计方法，该方法基于自主式系统构建了“感知、认知、决策、控制、执行、记忆、学习”自主运行机制，以模拟人类的认知过程，并基于数字孪生系统概念构建了物理-数字-交互空间协同架构；其次，为应对交通系统的复杂特性，重点对数字空间中的数字孪生系统进行了多层级、多尺度、自主化设计，包括路网、区域、载运装备的多层级架构，统筹时间、空间、数据与逻辑等多尺度建模，并采用战略、战术和执行的运行逻辑，以及云端、边缘端和车端协同计算范式实现了自主化学习演进；最后，在智能避障、交叉口冲突协同和高峰路径规划等场景中验证了该文所提方法的有效性。结果表明：该文所提方法能有效支持交通系统全局态势推演，并保障载运装备在多层级和多尺度下安全、高效和自主运行。该文研究结果可为构建可靠和可进化的自主式交通系统提供参考。

Objective: Although digital twin technology has matured rapidly, prevailing transportation digital twins remain largely descriptive and reactive, focusing on static model replication and real-time data monitoring while lacking a closed-loop architecture capable of enabling end-to-end autonomous operation across the system's full lifecycle. Critically, current implementations omit persistent memory for experience retention and adaptive learning mechanisms for continuous evolution, leaving them fragile in complex, dynamic, or previously unseen operational contexts. Furthermore, dominant research paradigms optimize isolated components without cross-granularity coordination, thereby failing to realize system-level autonomy. To bridge these critical gaps, this study proposes a foundational design methodology for digital twins of autonomous transportation system (ATS), explicitly engineered to embed cognition, memory, and evolutionary intelligence into the core architecture. Methods: This study proposed a digital twin system design methodology for the autonomous operation of transportation systems by integrating autonomous operational mechanisms and digital twin technology with existing transportation systems, thereby providing support for realizing ATS. First, based on autonomous systems, a "perception, cognition, decision-making, control, execution, memory, and learning" autonomous operational mechanism was proposed to simulate human cognitive processes, which is then integrated into transportation systems to define the concept of ATS. Next, leveraging the concept of digital twin systems, a collaborative architecture comprising the physical, digital, and interactive spaces of transportation systems was constructed, highlighting the importance of digital space design in achieving ATS. To integrate autonomous mechanisms into transportation systems and address their inherent complexity, this study applied the autonomous operational framework to design digital twin systems in the digital space at multiple levels and scales, enabling autonomy. A multilevel architecture encompassing road networks, road segments, and transport equipment was designed to enable coordinated management across macroscopic, mesoscopic, and microscopic granularities. The study integrated multiscale modeling and simulation capabilities across spatial, temporal, data, and logical dimensions, supporting a complete decision-making process from system-level situation awareness to individual vehicle behavior prediction. By leveraging distributed computing across cloud, edge, and end devices, the system achieved distributed deployment and efficient execution of cognitive, decision-making, control, memory, and learning functions, enabling autonomous evolution and learning. Finally, the proposed system was validated through application scenarios including intelligent obstacle avoidance, intersection conflict coordination, and peak-hour route planning. Results: Based on the proposed design methodology for digital twin systems supporting autonomous transportation operations, three representative scenarios were evaluated. In intelligent obstacle avoidance scenarios, after detecting obstacles, vehicles rapidly evaluated multiple avoidance and driving options through coordinated decision-making among vehicle-, road segment-, and road network-level digital twins, generating optimal avoidance strategies, thereby improving decision accuracy and control responsiveness. In intersection scenarios, coordinated signal and vehicle control enabled by digital twins allowed the system to better adapt to changing traffic demands across different time periods, weather conditions, and unexpected events, enhancing both intersection throughput and vehicle efficiency. In peak-hour scenarios, collaborative path planning across these digital twins improved the autonomous operational capability and travel efficiency of transport systems during high-demand periods. Conclusions: The proposed methodology transcends conventional digital twin paradigms by embedding cognitive architecture, long-term memory, and self-improving learning capabilities directly into the system's design. It provides a theoretically grounded framework and an implementation-ready blueprint for building ATS that are not only safe and efficient at runtime but also resilient to uncertainty, capable of lifelong learning, and able to evolve system-level intelligence over time. This study lays essential groundwork for next-generation transportation systems that anticipate, adapt, and autonomously improve.