A key challenge in agent-based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate the quality of the simulated travel patterns. These locations are typically represented in origin-destination matrices that are sampled using coarse travel surveys. This is because fine-grained trip profiles are scarce and fragmented due to privacy and cost reasons. The discrepancy between data and sampling resolutions renders agent traits non-identifiable due to the combinatorial space of data-consistent individual attributes. This problem is pertinent to any agent-based inference setting where the latent state is discrete. Existing approaches have used continuous relaxations of the underlying location assignments and subsequent ad-hoc discretisation thereof. We propose a framework to efficiently navigate this space offering improved reconstruction and coverage as well as linear-time sampling of the ground truth origin-destination table. This allows us to avoid factorially growing rejection rates and poor summary statistic consistency inherent in discrete choice modelling. We achieve this by introducing joint sampling schemes for the continuous intensity and discrete table of agent trips, as well as Markov bases that can efficiently traverse this combinatorial space subject to summary statistic constraints. Our framework's benefits are demonstrated in multiple controlled experiments and a large-scale application to agent work trip reconstruction in Cambridge, UK.

翻译：智能体移动仿真的一个关键挑战在于合成个体智能体的社会经济特征。此类特征包含智能体活动的位置信息，这些位置决定了模拟出行模式的质量。由于隐私和成本原因，细粒度出行数据稀缺且零散，因此这些位置通常由基于粗糙出行调查采样的起讫点矩阵表示。数据与采样分辨率之间的差异导致智能体属性因数据一致个体属性的组合空间而无法识别。该问题适用于任何潜在状态为离散的基于智能体的推断场景。现有方法采用底层位置分配的连续松弛及随后的特设离散化处理。我们提出一个高效导航该空间的框架，在改善重建覆盖率和线性时间采样的同时，实现真实起讫点表的线性时间采样。这使我们能够避免离散选择建模中固有的阶乘级增长拒绝率和较差的汇总统计一致性。通过引入智能体出行连续强度与离散表格的联合采样方案，以及能够在汇总统计约束下高效遍历该组合空间的马尔可夫基，我们实现了这一目标。该框架的优势在多个对照实验及英国剑桥智能体工作出行重建的大规模应用中得到了验证。