AI 中文总结
针对交通波动下公共交通性能下降问题，提出基于NSGA-III的先预测后优化线路重设计框架，在保留高线路重叠度的同时提升用户性能、降低运营成本并保持连通性，推动公共交通从静态规划转向自适应设计。
AI 中文摘要
公共交通（PT）线路传统上被设计为在标称交通条件下优化性能，但在实际运营中，交通条件常偏离标称状态，导致性能大幅下降。现有适应机制通常依赖停止越站等被动干预措施，在大规模或反复出现的交通波动下效果不足，增量调整也可能无法满足需求。本文评估了更深层次的结构重设计对保持性能的潜力，提出一种在高交通波动下主动重设计公共交通网络合适部分的方法，以避免运营商和用户性能恶化。该方法采用“先预测后优化”范式，基于交通预测使用非支配排序遗传算法III（NSGA-III）重新配置公共交通线路；为确保运营可行性并避免过度结构变更，要求原始网络与重设计网络之间的边雅卡尔重叠度较高。为评估预测误差，构建了基于成熟深度学习预测器扩散卷积循环神经网络（在真实数据上训练）的误差统计模型。在Mandl基准测试和大规模北京网络上的计算结果表明，可控的公共交通线路重设计在高交通波动下可显著提升以用户为中心的性能、降低运营成本，同时限制拓扑变化：在北京高变异性网络中，平均出行时间提升达25.8%，同时保留超过85%的线路重叠度；与会破坏大量起讫点（OD）对连通性的停止越站基线不同，本文的重设计方案保留了完整的OD连通性。这些结果支持从静态规划向连续自适应公共交通网络设计的转变。
英文摘要
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.
