用于离散选择建模的神经贝叶斯结构学习
Neural-Bayesian Structure Learning for Discrete Choice Modeling
AI总结：
本文提出Neural-BSL框架，耦合可微结构学习与离散选择估计，经首尔、伦敦数据评估，其预测性能与传统基准相当，可恢复行为一致的依赖结构，能有效分析干预措施的下游影响。
AI中文摘要：
传统离散选择模型与机器学习模型主要基于观测数据进行估计，通常将解释性协变量视为并行输入，不存在当某一属性被刻意改变时，如何调整相关属性的内部机制。本文提出神经贝叶斯结构学习（Neural-Bayesian Structure Learning, Neural-BSL），该框架在单一可微流程中耦合可微结构学习与基于随机效用的离散选择估计。为防止互斥选择结果扭曲恢复的属性结构，将观测到的选择作为特定替代方案的效用比较保留在图外，同时联合学习属性结构与随机效用参数。学习到的结构通过结构加权属性交互作用进入选择模型，并为干预措施通过下游属性的传播提供结构基础。干预措施的评估方式为：更新被干预属性，按拓扑顺序传播其模型隐含的下游变化，随后重新计算效用与选择概率，从而得到预测的模式份额响应及相关的下游出行者或出行属性变化。我们使用来自首尔的陈述偏好数据与来自伦敦的显示偏好数据对Neural-BSL进行评估，其预测性能与传统基准相当，同时恢复了行为一致的依赖结构。在政策场景中，通过学习到的结构传播干预措施会改变预测的模式间再分配，同时揭示这些响应背后的下游出行者与出行调整。
英文摘要：
Conventional discrete choice and machine learning models are estimated primarily from observational data and typically treat explanatory covariates as parallel inputs, providing no internal mechanism for determining how related attributes should adjust when one is deliberately changed. This paper proposes Neural-Bayesian Structure Learning (Neural-BSL), a framework coupling differentiable structure learning with random-utility-based discrete choice estimation in a single differentiable procedure. To prevent mutually exclusive choice outcome from distorting the recovered attribute structure, the observed choice is maintained outside the graph as an alternative-specific utility comparison, while the attribute structure and random-utility parameters are learned jointly. The learned structure enters the choice model through structure-weighted attribute interactions and provides the structural basis for propagating interventions through downstream attributes. An intervention is evaluated by updating the intervened attribute, propagating its model-implied downstream changes in topological order, and then recomputing utilities and choice probabilities. This yields both predicted mode-share responses and the associated changes in downstream traveler or trip attributes. We evaluate Neural-BSL using stated-preference data from Seoul and the revealed-preference data from London. Neural-BSL achieves predictive performance comparable to conventional benchmarks while recovering behaviorally coherent dependency structures. Across policy scenarios, propagating interventions through the learned structure changes the predicted redistribution across modes while exposing the downstream traveler and trip adjustments underlying those responses.
