The paper introduces RSTGCN, a Railway-centric Spatio-Temporal Graph Convolutional Network that predicts average arrival delays for all incoming trains at a specific station during a given time period. It incorporates train frequency-aware spatial attention and other architectural innovations to improve predictive accuracy. Experiments on a newly released dataset covering 4,735 Indian Railway stations show RSTGCN outperforms state‑of‑the‑art baselines by 18% in MAE, 14% in MAPE, and 1–8% in RMSE.
arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.
arXiv:2601. 16592v2 Announce Type: replace-cross Abstract: Train delays result from complex interactions between operational, technical, and environmental factors.
arXiv:2607. 24885v1 Announce Type: cross Abstract: Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility.
arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.
arXiv:2608.29658v1 Announce Type: cross Abstract: Accurate traffic flow prediction is fundamental to intelligent transportation systems, playing a pivotal role in urban mobility optimization and smar...
arXiv:2606. 13119v2 Announce Type: replace-cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
arXiv:2606. 13119v1 Announce Type: cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
The paper presents a machine‑learning approach to predict train delays in Finland using the Finland Integrated Train‑Weather (FI‑TW) dataset, which merges railway operational records with data from about 200 nationwide weather sensors. Three feature sets were tested with XGBoost at Oulu central station: full weather features, instant observations only, and derived weather categories. The category‑based features—hierarchical classes such as Blizzard, Heavy Snow, and Extreme Cold—yielded the best performance, achieving an R² of 0.78, RMSE of 8.5 min, and MAE of 3.7 min, an 11 % R² improvement and 10 % error reduction over the other configurations.
arXiv:2608.20980v1 Announce Type: new Abstract: Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the...
arXiv:2607. 13108v1 Announce Type: cross Abstract: Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting.
The paper introduces LoReST, a Local-Region Spatial Temporal network designed for large-scale traffic forecasting. LoReST captures local spatial heterogeneity by relation-aware aggregation within node neighborhoods and incorporates cross-region context through mean pooling, inter-region attention, and broadcasting back to nodes. Experiments on the LargeST benchmark demonstrate significant improvements, reducing MAE, RMSE, and MAPE by 4.78%, 3.60%, and 5.75% respectively.
The paper introduces a gated graph attention network (GAT) to better explain spatial information flow in short‑term traffic forecasting models. By adding a gate that learns how much of a sensor’s updated state comes from its neighbors versus itself, the authors can progressively withdraw neighbor information and observe its impact on accuracy. Applied to the ST‑MetaNet architecture on data from 498 loop detectors in England, the gated GAT shows that neighbor influence is higher for busy sensors, improves accuracy slightly with mild regularisation, and reveals that the two GAT layers are largely redundant.