发表机构
Chung-Ang University（中央大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出决策聚焦神经优化框架，将路线复现建模为潜在成本上的最短路径问题，结合感知编码和约束优化层，利用iMLE和正则化实现端到端训练，实证优于基线模型。
AI 中文摘要
本研究将个体路线复现问题形式化为在学习的驾驶员特定潜在链路成本上的最短路径问题。核心思想是，一旦从上下文信息中推断出这些潜在成本，就可以在不枚举备选路线集的情况下复现观测到的路线。我们提出了一种神经管道，包括一个感知模型，该模型将上下文协变量（包括个体特征、行程特定属性和网络级交通状态）嵌入到个性化链路成本中。随后是一个约束优化（CO）层，它基于这些估计的成本确定最短路径（SP）。为了实现端到端训练，我们采用决策聚焦学习来使预测的最短路径与观测到的路线对齐。隐式最大似然估计（iMLE）提供了包含不可微CO层的损失函数的近似梯度。此外，一个正则化项将潜在成本分布锚定到观测到的链路行程时间的经验尺度上，从而缓解了最短路径监督中固有的尺度模糊性。实证评估表明，所提出的框架在路径复现方面优于基线路线选择模型。学习到的潜在成本，被解释为感知旅行成本的代理，为异质路线选择提供了合理的解释。
英文摘要
This study formulates individual route reproduction as a shortest-path problem over learned driver-specific latent link costs. The central idea is that, once such latent costs are inferred from contextual information, observed routes can be reproduced without enumerating alternative route sets. We propose a neural pipeline that includes a perception model that embeds context covariates, which comprises individual characteristics, trip-specific attributes, and network-level traffic states, into the personalized link costs. A constrained optimization (CO) layer, which determines the shortest path (SP) based on these estimated costs, follows the perception encoder. To enable end-to-end training, we employ decision-focused learning to align the predicted shortest paths with observed routes. The implicit maximum likelihood estimation (iMLE) provides an approximate gradient of the loss function that contains the non-differentiable CO layer. Furthermore, a regularization term anchors the latent cost distribution to the empirical scale of observed link travel times, mitigating the scale ambiguity inherent in shortest-path supervision. Empirical evaluations demonstrate that the proposed framework outperforms baseline route choice models in path reproduction. The learned latent costs, interpreted as proxies for perceived travel costs, provide plausible explanations for heterogeneous route choices.
