Predict-then-Optimize Framework for Public Transport Line Redesign under Fluctuating Traffic Conditions

Abstract

Public Transport (PT) lines are traditionally designed to optimize performance under nominal traffic conditions. In practice, operating conditions frequently deviate from nominal ones, leading to substantial performance deterioration. Existing adaptation mechanisms typically rely on reactive interventions, such as stop-skipping, which are insufficient under large or recurrent traffic fluctuations. In such contexts, incremental adjustments may not suffice. This paper evaluates the potential of deeper structural redesigns to preserve performance. We propose a method to proactively redesign appropriate parts of PT networks under high traffic fluctuations that would otherwise deteriorate operator and user performance. We adopt a predict-then-optimize paradigm in which PT lines are reconfigured based on traffic forecasts using the Non-dominated Sorting Genetic Algorithm III (NSGA-III). To ensure operational feasibility and avoid excessive structural changes, we enforce high Jaccard edge overlap between the original and redesigned networks. To assess prediction inaccuracies, we construct a statistical model of errors from a well-established deep learning predictor, the Diffusion Convolutional Recurrent Neural Network, trained on real-world data. Computational results on Mandl's benchmark and the large-scale Beijing network show that controlled PT line redesign yields substantial user-centric performance gains and operational cost reductions under high traffic fluctuations while limiting topological changes. On the Beijing network under high variability, average travel time improves by up to 25.8% while preserving over 85% line overlap. Unlike stop-skipping baselines, which break connectivity for many OD pairs, our redesign preserves full OD connectivity. These results support a shift from static planning toward continuous and adaptive PT network design.