多模式交通票价联盟设计：面向快速收益的大规模优化
Multimodal Transportation Pricing Alliance Design: Large-Scale Optimization for Rapid Gains
AI总结：
针对公交与出行即服务（MOD）联盟，提出联合优化多模式网络票价与折扣的大规模两阶段分解算法，兼顾公平与乘客中心优先级，实现快速收益与系统利用率提升。
摘要：
公交机构有机会将某些服务外包给成熟的出行即服务（Mobility-on-Demand, MOD）提供商。此类联盟能够提升服务质量、覆盖范围和客流量；降低公共部门成本和车辆排放；并整合乘客体验。为增强此类联盟的有效性，我们开发了一个票价设定模型，该模型在多模式网络中对票价和折扣进行联合优化。我们使用离散选择模型刻画通勤者的出行决策，由此产生一个大规模、混合整数、非凸的优化问题。为求解这一挑战性问题，我们提出了一种两阶段分解方法：第一阶段进行票价决策，第二阶段对票价折扣和乘客出行决策进行混合整数线性优化。为求解该分解问题，我们开发了一种新的求解方法，结合了定制坐标下降、简约的第二阶段评估以及基于特殊有序集的插值。该方法通过基于倾斜遍历、随机化和热启动的加速技术加以增强，显著优于算法基准。不同的联盟优先级会导致定性不同的票价设计：统一票价减少总车辆行驶里程，而基于地理信息的折扣则提升乘客满意度。该模型能恰当响应公平导向和乘客中心优先级，改善系统利用率并降低低收入和长途通勤者的价格。我们的利润分配机制改善了两种类型运营商的收益，从而激励以利润为导向的MOD运营商采纳公交优先事项。
英文摘要：
Transit agencies have the opportunity to outsource certain services to established Mobility-on-Demand (MOD) providers. Such alliances can improve service quality, coverage, and ridership; reduce public sector costs and vehicular emissions; and integrate the passenger experience. To amplify the effectiveness of such alliances, we develop a fare-setting model that jointly optimizes fares and discounts across a multimodal network. We capture commuters' travel decisions with a discrete choice model, resulting in a large-scale, mixed-integer, non-convex optimization problem. To solve this challenging problem, we develop a two-stage decomposition with the pricing decisions in the first stage and a mixed-integer linear optimization of fare discounts and passengers' travel decisions in the second stage. To solve the decomposition, we develop a new solution approach combining tailored coordinate descent, parsimonious second-stage evaluations, and interpolations using special ordered sets. This approach, enhanced by acceleration techniques based on slanted traversal, randomization and warm-start, significantly outperforms algorithmic benchmarks. Different alliance priorities result in qualitatively different fare designs: flat fares decrease the total vehicle-miles traveled, while geographically-informed discounts improve passenger happiness. The model responds appropriately to equity-oriented and passenger-centric priorities, improving system utilization and lowering prices for low-income and long-distance commuters. Our profit allocation mechanism improves outcomes for both types of operators, thus incentivizing profit-oriented MOD operators to adopt transit priorities.
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。
