超越图结构：一种自适应元学习器融合可解释性、天气与动态信息用于稳健的公交ETA预测
Beyond the Graph: An Adaptive Meta-Learner Fuses Explainability, Weather, and Dynamics for Robust Bus ETA Prediction
AI总结：
针对公交ETA预测中非线性动态与天气干扰问题，提出自适应混合框架HYB(nm)，融合五种模型并经元学习器动态加权，在加尔各答超4000次行程数据上实现高鲁棒性与先进准确性，并提供可扩展的HYB(k)架构。
AI中文摘要：
准确的公交预计到达时间（ETA）预测对于城市出行、乘客满意度和交通效率至关重要，然而现有模型在面对非线性时空动态、数据稀疏性以及天气等因素时表现不佳。本文提出HYB(nm)，一种自适应混合集成框架，通过一个适应实时上下文的元学习器，动态融合五种互补模型——历史基线模型（MST-AV）、周期性时间模式分析（GDRN-DFT）、用于非线性动态的Koopman神经算子（KOOP-NET）、天气集成特征工程神经网络（FENN）以及实时图卷积网络（MGCN）。在来自加尔各答三条公交线路、包含超过4000次行程的GPS和天气数据上进行评估，该框架利用其各组件的个体优势（例如，MST-AV的低延迟可解释性、FENN的天气鲁棒性以及MGCN的网络动态捕捉能力），实现了HYB(2)的卓越鲁棒性、与领先图神经网络相媲美的先进准确性，并在不同预测时间范围和运行条件下实现了稳定性与效率的平衡权衡。可扩展的HYB(k)架构为公交运营机构提供了灵活的工具，从经济的单一模型到定制的高保真混合模型，推进了预测性、公平的城市交通。
英文摘要：
Accurate bus Estimated Time of Arrival (ETA) prediction is vital for urban mobility, passenger satisfaction, and transit efficiency, yet existing models falter against nonlinear spatiotemporal dynamics, data sparsity, and factors such as weather. This paper proposes HYB(nm), an adaptive hybrid ensemble framework that dynamically fuses five complementary models - a historical baseline (MST-AV), periodical temporal pattern analysis (GDRN-DFT), Koopman Neural Operators for nonlinear dynamics (KOOP-NET), weather-integrated feature-engineered neural networks (FENN), and real-time graph convolutional networks (MGCN) - via a meta-learner attuned to real-time context. Evaluated on GPS and weather data from three Kolkata bus routes comprising more than 4,000 trips, the framework leverages the individual strengths of its components (for example, the low-latency explainability of MST-AV, the weather resilience of FENN, and the network-dynamics capture of MGCN) to deliver the superior robustness of HYB(2), state-of-the-art accuracy rivalling leading graph neural networks, and balanced trade-offs in stability and efficiency across prediction horizons and operating conditions. The extensible HYB(k) architecture equips transit agencies with flexible tools, ranging from economical single models to tailored high-fidelity hybrids, advancing predictive, equitable urban transport.
