先估计后预测：出行需求预测的凸优化形式

中文摘要 AI 辅助

出行需求预测对于评估大型基础设施项目至关重要，然而传统的四阶段顺序流程可能在出行分布、方式选择和交通分配之间产生不一致。尽管组合模型解决了这些不一致性，但其实际应用受到简化行为假设、计算负担以及缺乏统一参数估计框架的限制。我们提出了一种凸规划方法，在分层扩展logit模型中整合目的地、方式和路径选择。最优原始解刻画了联合出行需求均衡，而最优对偶变量则恢复了品味系数、替代特定常数以及目的地和方式层面的尺度参数。该模型通过嵌套logit捕捉方式相关性，通过路径规模logit捕捉路径重叠。其凸结构提供了全局最优性保证，并能够利用现成的锥优化求解器高效求解。观测到的出行模式通过矩约束和条件熵约束纳入，而路径层面的离散参数则使用部分链路计数单独校准。在八个基准网络上的数值实验证明了该公式的可扩展性和计算效率。

英文摘要

Travel demand forecasting is essential for evaluating large-scale infrastructure projects, yet the traditional sequential four-step process can produce inconsistencies across trip distribution, mode choice, and traffic assignment. Although combined models address these inconsistencies, their practical use has been limited by simplified behavioral assumptions, computational burden, and the lack of a unified parameter-estimation framework. We propose a convex programming approach that integrates destination, mode, and route choices within a hierarchical extended logit model. The optimal primal solution characterizes the joint travel-demand equilibrium, while the optimal dual variables recover taste coefficients, alternative-specific constants, and destination- and mode-level scale parameters. The model captures mode correlations through nested logit and route overlap through path-size logit. Its convex structure provides global optimality guarantees and enables efficient solution using off-the-shelf conic solvers. Observed travel patterns are incorporated through moment and conditional-entropy constraints, while the route-level dispersion parameter is calibrated separately using partial link counts. Numerical experiments on eight benchmark networks demonstrate the scalability and computational efficiency of the formulation.

发表机构

• UCLA（加州大学洛杉矶分校）
• MIT（麻省理工学院）
• Cornell University（康奈尔大学）

机构由 AI 辅助整理，请以论文原文为准。