AI 中文总结
该综述梳理用于交通预测的NAS方法，分析其搜索策略、挑战与未来方向，为解决交通预测模型人工设计泛化差等问题提供系统性方案。
AI 中文摘要
交通预测是智能交通系统的核心任务，支撑自适应信号控制、路径引导、网约车调度等应用。深度学习模型（包括图卷积网络、循环网络、Transformer）在标准基准上取得优异结果，但其架构由人工设计，需大量专家精力，且生成的模型在不同城市和数据集间泛化能力往往较差。神经架构搜索（NAS）为人工设计提供了系统性替代方案，它可自动搜索深度学习模型的候选架构，无需人工反复试错即可找到匹配交通数据时空结构的设计。本综述对应用于交通预测的NAS方法进行梳理，按搜索策略分为基于梯度的方法、进化方法和单次权重共享方法三类。对每一类，我们分析其搜索空间如何设计以覆盖时空交通算子，以及搜索策略如何权衡成本与架构质量。我们还探讨了开放挑战：大型路网的计算可扩展性、手动搜索空间设计、跨城市泛化、动态图结构，以及面向时空基础模型的NAS这一开放问题，并明确了未来研究方向。
英文摘要
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 strong results on standard benchmarks, but their architectures are designed by hand, requiring significant expert effort and producing models that often generalize poorly across cities and datasets. Neural Architecture Search (NAS) offers a systematic alternative to manual design. It automates the search over candidate architectures of deep learning models, finding designs that match the spatial-temporal structure of traffic data without manual trial and error. This survey reviews NAS methods applied to traffic prediction, organized by search strategy: gradient-based methods, evolutionary methods, and one-shot weight-sharing methods. For each category, we analyze how the search space is designed to cover spatial and temporal traffic operators, and how the search strategy balances cost against architecture quality. We also discuss open challenges, computational scalability to large road networks, manual search space design, cross-city generalization, dynamic graph structure, and the open question of NAS for spatial-temporal foundation models, and identify directions for future research.
Comments8 pages, 3 tables. Accepted at UrbCom 2026, the 8th International Workshop on Urban Computing, co-located with IEEE DCOSS-IoT 2026
