AI 中文总结
针对大规模公共交通线路规划中考虑需求响应性这一复杂问题，提出可扩展的ALNS算法，联合优化线路和频率，动态生成候选线路并评估解决方案，在丹麦欧登塞网络验证其适用性，优化后提升了服务水平，凸显校准需求模型的重要性。
AI 中文摘要
需求响应性是公共交通线路规划中的重要考量因素，因为网络设计和服务质量会影响乘客需求。但考虑这种相互作用会使本就具有挑战性的组合优化问题更加复杂。为应对这一挑战，我们提出一种可扩展的自适应大邻域搜索（ALNS）算法，用于具有内生需求的大规模线路规划。该算法在考虑乘客模式选择、乘客分配和车辆容量的同时，联合优化线路和频率。在搜索过程中动态生成候选线路，并使用用于乘客分配和需求估计的嵌入式评估程序以及用于频率优化的专用局部搜索程序来评估解决方案。我们在丹麦欧登塞的公共交通网络上对该方法进行了评估，该网络包含约1800个起讫点对。计算结果表明该方法适用于实际的大规模实例。优化后的网络将资源集中在更少、频率更高的服务上，将平均发车间隔从约41分钟减少到6.8 - 13分钟，同时大幅提高了公共交通客流量。此外，结果表明网络设计对乘客行为假设高度敏感，突出了在将需求响应性纳入线路规划时仔细校准需求模型的重要性。
英文摘要
Demand responsiveness is an important consideration in public transport line planning, as network design and service quality influence passenger demand. However, accounting for this interaction further complicates an already challenging combinatorial optimization problem. To address this challenge, we propose a scalable Adaptive Large Neighborhood Search (ALNS) algorithm for large-scale line planning with endogenous demand. The algorithm jointly optimizes lines and frequencies while accounting for passenger mode choice, passenger assignment, and vehicle capacities. Candidate lines are generated dynamically throughout the search, and solutions are evaluated using an embedded evaluation procedure for passenger assignment and demand estimation, together with a dedicated local search procedure for frequency optimization. The proposed methodology is evaluated on the public transport network of Odense, Denmark, comprising approximately 1,800 origin-destination pairs. Computational results demonstrate the applicability of the approach to realistic, large-scale instances. The optimized networks concentrate resources on fewer, higher-frequency services, reducing average headways from approximately 41 minutes to 6.8-13 minutes while substantially increasing public transport ridership. Furthermore, the results show that network design is highly sensitive to assumptions regarding passenger behavior, highlighting the importance of carefully calibrated demand models when incorporating demand responsiveness into line planning.
Comments47 pages, 7 figures
