Deep Learning Approach for Route ETA Prediction in Navigation
The article proposes a deep‑learning framework that uses an LSTM to predict segment‑level travel times and fully‑connected layers to aggregate them into a full‑route ETA, demonstrating on Beijing data a 28.2% MSE reduction and superior accuracy over traditional regressors by capturing temporal and network dependencies.