The dynamics of urban transportation can be captured using activity-based models, which rely on travel demand data to get a comprehensive understanding of urban mobility. This data is usually derived from population samples and Household Travel Surveys (HTSs), which can be expensive and as a result, are conducted only every 5 to 10 years. Moreover, due to their limited reach, they are not adapted to represent the spatio-temporal structure of the flows of the total population. This calls for complementary data sources that could be used to update old surveys to cut costs and to estimate the global spatial mobility behavior of the population. In this paper, we propose steps in the state-of-the-art pipeline for travel demand synthesis with an approach for the temporal calibration and the location attribution based on time-dependent origin–destination (OD) matrices. These matrices describe the flows between zones of a city. This methodology is illustrated on the city of Lyon, France, with OD matrices estimated from the mobile phone activity of the subscribers of French telecom operator Orange. We explore how the spatialization can be performed using various probabilistic graph models whose parameters are evaluated via the OD matrices. The structure of the models enforces the consistency of the locations with the chains of activities, such as the fact that two “home” activities must have the same location. Multiple models are proposed, corresponding to different compromises between the two potentially incompatible sources that are HTS and mobile data. We show that while a very naive spatialization approach allows the generation of synthetic travel demand that perfectly fits the flows described by the OD matrices without respecting the consistency of the locations, the other proposed approaches offer much more realistic agendas at the expense of only small discrepancies with the mobile data.

城市交通的动态可以使用基于活动的模型来捕获，该模型依靠出行需求数据来全面了解城市交通。这些数据通常来自人口样本和家庭旅行调查 (HTS)，这些调查的成本可能很高，因此每 5 到 10 年才进行一次。此外，由于其范围有限，它们不适合代表总人口流动的时空结构。这就需要补充数据源，这些数据源可用于更新旧的调查，以降低成本并估计人口的全球空间流动行为。在本文中，我们提出了最先进的旅行需求合成流程中的步骤，以及基于时间相关的起点-目的地（OD）矩阵的时间校准和位置归因方法。这些矩阵描述了城市区域之间的流动。该方法以法国里昂市为例，根据法国电信运营商 Orange 用户的移动电话活动估算出 OD 矩阵。我们探索如何使用各种概率图模型来执行空间化，其参数通过OD 矩阵进行评估。模型的结构强制执行位置与活动链的一致性，例如两个“家庭”活动必须具有相同的位置。提出了多种模型，对应于 HTS 和移动数据这两个可能不兼容的源之间的不同折衷方案。 我们表明，虽然一种非常简单的空间化方法允许生成完全符合 OD 矩阵描述的流量的综合旅行需求，而不考虑位置的一致性，但其他提出的方法提供了更现实的议程，但只存在很小的差异与移动数据。