Accurate bus arrival time prediction supports passenger information services and transit dispatching, but irregular sampling and time-varying traffic conditions make the task strongly nonlinear. This paper proposes a Spatial Attention Multi-Scale Traffic-State Gated Long Short-Term Memory (SA-MS-TG-LSTM) network. Seven trajectory features are constructed from Global Positioning System (GPS) records, including acceleration, congestion index, and speed volatility. Scale-preserving Spatial Feature Attention (SFA) first reweights the feature dimensions, and three Traffic-State Gated Long Short-Term Memory (TG-LSTM) branches subsequently encode the latest 5, 10, and 15 time steps. Their outputs a... More >

Intermittent daily rainfall requires temporal pattern extraction and correction of newly observed prediction errors. This paper presents an event-triggered convolutional neural network--long short-term memory model with prediction-error closed-loop feedback (ECF-CNN-LSTM) for seasonal one-day-ahead rainfall forecasting. A 30-day window supplies 31 causal rainfall-history and calendar features. A binary gate controls a zero-initialized, bounded residual and learns from the expected prediction errors of its inactive and active alternatives. A separate chronological controller uses each newly observed error to adjust subsequent forecasts. The single-series Tengzhou, Shandong dataset contains 97... More >