摘要
平行交通是一种实现智能交通管理与控制的综合性范式,致力于解决人类行为和社会因素的复杂性问题。近年来,基础模型(foundational models, FMs)的崛起为平行交通的实现提供了新的可能。但这种模型固有的知识陈旧、“幻觉”现象以及“黑盒”特性削弱了其决策的可靠性和可信度。为解决这一问题,提出一种基于检索增强生成与思维链提示(chain-of-thought prompting)的平行交通框架TransRAG。该框架由紧密协作的存储层、管理层和执行层组成,旨在为用户提供个性且多样化的交通服务。其中,存储层引入的外部知识增强了管理层中基础模型的性能,以实现复杂的计算实验。执行层中人工交通系统与实际交通系统的虚实交互使得管理层的决策得到持续优化,从而实现动态知识更新和灵活的策略调整,以适应不断变化的交通环境。此外,TransRAG通过区块链、智能合约和缓存技术的集成,能够有效应对单点故障、隐私泄露以及数据访问延迟等问题,从而加速推进向“6S”交通5.0的全面迈进。
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Jing YANG drafted the paper. Xingyuan DAI, Yisheng LV, Levente KOVÁCS, and Fei-Yue WANG helped organize the paper. Jing YANG revised and finalized the paper.
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Fei-Yue WANG is an executive associate editor-in-chief of Frontiers of Information Technology & Electronic Engineering, and he was not involved with the peer review process of this paper. All the authors declare that they have no conflict of interest.
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Project supported by the Science and Technology Development Fund of Macao SAR, China (No. 0093/2023/RIA2) and the National Natural Science Foundation of China (No. U1811463)
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Yang, J., Dai, X., Lv, Y. et al. TransRAG for parallel transportation: toward reliable and trustworthy transportation systems via retrieval-augmented generation. Front Inform Technol Electron Eng 26, 20–26 (2025). https://doi.org/10.1631/FITEE.2400800
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DOI: https://doi.org/10.1631/FITEE.2400800