发表机构
Ulsan National Institute of Science and Technology (UNIST); University of California, Los Angeles (UCLA)（蔚山科学技术院; 加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个联合优化服务菜单、票价和车辆行程的实时框架，利用GNL模型刻画乘客选择，并通过Fenchel对偶实现高效求解，显著提升运营商利润率和计算效率。
AI 中文摘要
按需出行（MoD）平台可以提供专享、拼车和公共交通接驳等服务，以满足不同乘客的需求。然而，确定提供哪些服务以及以何种价格提供具有挑战性，因为盈利能力取决于乘客选择和运营可行性。本文提出一个实时框架，联合优化单个MoD请求的服务菜单、票价和车辆行程计划。乘客选择使用广义嵌套Logit（GNL）模型建模，该模型基于车辆共享和公共交通使用情况捕捉服务选项之间的相关替代性。为了求解由此产生的非线性票价优化问题，我们利用Fenchel对偶将非线性目标从票价域转换到选择概率域，得到一个包含广义熵项的凹最大化问题。我们使用纽约市真实出行记录进行运营仿真来评估该框架。与仅优化服务菜单和仅优化票价相比，联合组合设计和定价分别将运营商服务利润率提高了28.4%和8.5%。此外，所提出的重构方法比直接在票价空间中求解原始定价问题快最多18.8倍，提高了实时MoD运营的计算效率。
英文摘要
Mobility-on-demand (MoD) platforms can offer exclusive, pooled, and transit-connected services to accommodate different passenger needs. However, determining which services to offer and at what prices is challenging because profitability depends on both passenger choices and operational feasibility. This paper proposes a real-time framework that jointly optimizes service menus, fares, and vehicle trip plans for individual MoD requests. Passenger choices are modeled using a generalized nested logit (GNL) model that captures correlated substitution among service options based on vehicle sharing and transit use. To solve the resulting nonlinear fare-optimization problem, we use Fenchel duality to transform the nonlinear objective from the fare domain to the choice-probability domain, yielding a concave maximization involving a generalized entropy term. We evaluate the framework through operational simulations using real-world New York City trip records. Joint portfolio design and pricing improve operator service margins by 28.4% and 8.5% compared with optimizing only service menus and only fares, respectively. Furthermore, the proposed reformulation is up to 18.8 times faster than solving the original pricing problem directly in fare space, improving computational efficiency for real-time MoD operations.
