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English(EN) Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions

综述详述交通预测模型的神经架构搜索

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新发表在arXiv上的综述论文探讨了神经架构搜索（NAS）在交通预测中的应用。该论文详细介绍了NAS如何自动化深度学习模型（如图卷积网络、循环神经网络和Transformer）的设计，以更好地捕捉交通数据的时空结构。它将NAS方法分为基于梯度、进化和一次性权重共享方法，同时还强调了计算可扩展性和跨城市泛化等挑战。 AI

影响 自动化交通预测的深度学习模型设计，有望提高智能交通系统的效率和泛化能力。

排序理由 该集群包含两篇相同的arXiv预印本，详细介绍了一篇关于特定研究主题的综述论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

综述详述交通预测模型的神经架构搜索

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该集群包含两篇相同的arXiv预印本，详细介绍了一篇关于特定研究主题的综述论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Truong Giang Vu, Li Yang, Richard W. Pazzi ·

    面向交通预测的神经架构搜索：方法、挑战与未来方向综述

    arXiv:2607.26467v1 Announce Type: new Abstract: Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional network…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Richard W. Pazzi ·

    面向交通预测的神经架构搜索：方法、挑战与未来方向综述

    Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional networks, recurrent networks, and Transformers, achieve…

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