Sep 2026· Journal of Transportation Engineering Part A Systems· 0 citations· 37 references
TL;DR
A novel spatiotemporal Kalman filter network (ST-KFNet) framework for metro demand forecasting by integrating an autoregressive integrated moving average module, a Kalman filter (KF) module, and a convolutional neural network (CNN)-based variational autoencoder (VAE) module is proposed.
Abstract
Accurate passenger demand prediction is crucial for efficient metro operations, yet remains challenging due to complex network structures, temporal variability, and travel uncertainties. This paper proposes a novel spatiotemporal Kalman filter network (ST-KFNet) framework for metro demand forecasting by integrating an autoregressive integrated moving average (ARIMA) module, a Kalman filter (KF) module, and a convolutional neural network (CNN)-based variational autoencoder (VAE) module. ARIMA constructs the state prediction equation, further enhanced by a neural-network-based predictor. CNN-based VAE captures spatial dependencies and temporal dynamics, refining the KF’s state transition for improved accuracy. The KF then provides real-time, online predictions, effectively handling both stationary and nonstationary uncertainties. By combining the strengths of statistical modeling and deep learning, ST-KFNet achieves high predictive accuracy under normal conditions and robust performance under uncertainty, such as unplanned station closures. Empirical evaluations on a metro data set from Hangzhou show that ST-KFNet consistently outperforms existing benchmarks, including graph convolutional network (GCN)-based methods as well as ARIMA, KF, and CNN-based VAE models.
A Tensor Network Extended Kalman Filter (TNEKF) framework for short-term metro OD demand forecasting that consistently outperforms ARIMA, conventional EKF, and several state-of-the-art spatiotemporal prediction models in terms of MAE, RMSE, and MAPE.
Aijing Su, Bing Wu, Xiaoxing Fang· ISPRS International Journal...· 0 citations
Experiments on ERA5 data show that AMBHFN outperforms eight retrained baselines over the 0–23 h forecast horizon, with an average error reduction of more than 12%.
The developed scheme outperforms counterpart models, advocating its potential to enhance dynamic traffic management and intelligent signal coordination systems and enables more accurate short-term flow estimates, thereby reducing average vehicle waiting times and improving intersection-level signal responsiveness.
Hamza Murad Khan, Anwar Khan, Muhammad Imran Majid et al.· The Arabian journal for scie...· 0 citations
Accurate short-term traffic prediction is a critical component of intelligent transportation systems (ITS), yet it remains challenging due to nonlinear temporal dynamics, evolving spatial dependencies, and uncertainty in real-time urban traffic data. This paper proposes a novel uncertainty-aware deep ensemble spatiotemporal forecasting framework integrating Dynamic Graph Convolutional Networks (DGCN), Temporal Transformers, and CNN–LSTM hybrid models. A confidence-guided ensemble fusion strategy dynamically weights individual predictions using Bayesian uncertainty estimation. Experiments conducted on real-time Bhopal city traffic data demonstrate significant improvements over state-of-the-art baselines, achieving up to 90% performance gains during peak and abnormal traffic conditions.
This work develops a unified one-day-ahead probabilistic forecasting framework that aligns temporal resolution, reconstructs the unavailable inputs, and derives causal features, and compares a modular post-hoc residual-quantile scheme with an integrated in-model quantile-learning scheme.
S. Al-Shareeda, Gulcihan Ozdemir, H. Jeon· Electric power systems resea...· 0 citations
With the rapid growth of smart-building applications, accurate forecasting of building energy consump- tion faces two critical challenges: some operational features (e.g., occupancy or equipment load) cannot be predicted accurately or may be entirely unavailable due to sensor malfunctions at prediction time, and com- plex nonlinear, long-range temporal dependencies are difficult to capture with traditional machine-learning models. Conventional approaches, such as Seasonal AutoRegressive Integrated Moving Average with eXoge- nous regressors (SARIMAX), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), require extensive feature engineering, often assume linearity or stationarity, and show reduced accuracy when only sparse inputs are provided. To overcome these limitations, we introduce a Knowledge Distillation (KD) framework in which a GPT-style Transformer-based Building Energy Forecast- ing (GPT-BEF) teacher model is first trained on rich historical multivariate time series to capture complex temporal dependencies in building energy use. Its knowledge is then transferred into a reduced-feature GPT- BEF+KD student model that uses only two temperature features. The resulting GPT-BEF+KD model achieves approximately 25% lower Root Mean Squared Error (RMSE) than a two-feature RF baseline, sug- gesting its potential for lightweight forecasting under limited sensor availability. The analysis demonstrates that the student model effectively inherits part of the teacher’s ability to balance long-term and short-term temporal signals. By combining archive-driven full-feature learning with feature-constrained inference, this work provides an initial demonstration of GPT-style knowledge distillation for sparse-feature building en- ergy prediction and lays the foundation for future advances in multimodal fusion, closed-loop control, and uncertainty-aware energy management.
Yusen Wang, Xilei Dai, Zhenhong Lin et al.· Energy Use· 0 citations
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