一条规则统摄一切：探讨公共交通路线生成中的运输连通性以实现公平接入
One Rule to Bring Them All: Investigating Transport Connectivity in Public Transport Route Generation for Equitable Access
AI总结：
本文提出一种基于图神经网络和进化算法的混合方法，用于提升公共交通网络的连通性与公平性，通过改进可达性指标实现更均衡的资源分配。
摘要：
设计一个全市范围的公共交通网络面临双重挑战：在保证计算效率的同时，确保不同人口群体的空间公平性。我们探讨基于人工智能的优化混合神经进化方法，结合图神经网络与进化算法，是否能将 Transit Network Design Problem（TNDP）的解决方案从合成测试扩展到真实城市网络，同时保持社会公平性。我们的贡献是引入一种基于运输连通性的可达性指标，使优化基于公平可达性的原则，而非传统乘客和运营商成本之间的权衡。结果表明，在合成数据集上网络韧性有显著提升，同时突显了将网络生成应用于真实数据时的模糊性。
英文摘要：
Designing a city-wide public transport network poses a dual challenge: achieving computational efficiency while ensuring spatial equity for different population groups. We investigate whether AI-based optimization hybrid neuroevolutionary methods combining graph neural networks with evolutionary algorithms - can scale Transit Network Design Problem (TNDP) solutions from synthetic tests to real urban networks while preserving social fairness. Our contribution is to introduce a transport connectivity-aware accessibility metric that bases optimization on principles of equitable accessibility rather than traditional trade-offs between passenger and operator costs. The results show a noticeable improvement in network resilience by improving algebraic connectivity on synthetic datasets, and highlight the ambiguity of applying network generation to real data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
