Abstract
Accurate bus travel time prediction is a critical requirement for managing urban mobility and enabling reliable public transit operations. While existing research often relies on the fusion of multi-source, heterogeneous data, such approaches are less feasible in cities with limited access to external information. To address this gap, this study proposes a dataefficient approach to travel time prediction using only commonly available historical transit data. Specifically, a Hybrid Bidirectional Long Short-Term Memory (BiLSTM) model is applied to predict bus travel times without requiring external contextual inputs. The model is evaluated on real-world bus routes in Kandy and Astana, where it consistently outperforms attention-based ConvLSTM and stacked ensemble baselines. On Route 1 in Kandy, the proposed model achieves a Mean Absolute Error (MAE) of 5.44 seconds and a Mean Absolute Percentage Error (MAPE) of$\mathbf{2. 4 1 \%}$, markedly better than competing methods. Across three routes in Astana, the model reports MAE values between 42.16 and 50.45 seconds and MAPE between 34.23 % and 39.84 %, improving upon baselines by up to 20 %. Cross-validation further confirms the robustness of the approach, with consistently lower errors across all folds. The results show that accurate bus travel time prediction is achievable, even in a variety of data-limited environments, offering a practical and versatile solution for transport agencies.