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Beyond the Graph: An Adaptive Meta-Learner Fuses Explainability, Weather, and Dynamics for Robust Bus ETA Prediction

arXiv 2609.31667 ↗First posted 14 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 66 references that are in the map. First posted on arXiv 14 Sep 2026.

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References · 22

  1. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
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  7. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
  8. 2021Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformerscited by 248
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  10. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  11. 2019STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecastingcited by 60
  12. 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecastingcited by 41
10 more
  1. 2020Optimizing Neural Networks via Koopman Operator Theorycited by 33
  2. 2020AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecastingcited by 27
  3. 2023ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecastingcited by 22
  4. 2019Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecastingcited by 16
  5. 2021Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Networkcited by 9
  6. 2023MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecastingcited by 6
  7. 2021Hybrid Graph Embedding Techniques in Estimated Time of Arrival Taskcited by 4
  8. 2019To each route its own ETA: A generative modeling framework for ETA predictioncited by 3
  9. 2022Graph Neural Modeling of Network Flowscited by 3
  10. 2023Estimating time of arrival of vehicle fleets with GCN based traffic predictioncited by 1

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