Over the last decades, technological advances have allowed the capturing of travel behaviour at large-scale. Despite the unprecedented volume and the variety of personal mobility data, aggregate Origin-Destination (OD) matrices are still the most widespread means to organise and represent travel demand. Nonetheless, standard ODs cannot adequately capture significant elements affecting travel behaviour such as trip-interdependency and trip-chaining, therefore they are not particularly suitable for travel behaviour analysis at person-level. The currently presented modelling framework enables the in-depth study of personal mobility by firstly combining the trips present in OD matrices into home-based trip-chains (i.e. tours) and subsequently into sequences of activities (activity schedules). The above-mentioned process is completed based on advanced graph-theoretical and combinatorial optimisation concepts. The applicability of the methodology is meticulously verified through a large-scale test case where a set of multi-period, purpose dependant ODs is converted into realistic activity schedules able to incorporate more than 99% of the inputted travel demand. The accurate and highly detailed results showcase the significant potential of the proposed methodology to support the comprehensive analysis of travel behaviour at person level.

在过去的几十年中，技术进步已使人们能够大规模捕获旅行行为。尽管空前的数据量和个人出行数据的种类繁多，但汇总的出发地（OD）矩阵仍然是组织和表示旅行需求的最广泛的手段。但是，标准OD无法充分捕获影响旅行行为的重要因素，例如旅行相互依存性和旅行链，因此它们并不特别适用于人员级别的旅行行为分析。当前提出的建模框架通过首先将OD矩阵中存在的旅行组合到基于家庭的旅行链（即旅行）中，然后将其组合到活动序列（活动时间表）中，来实现对个人移动性的深入研究。上述过程是基于高级图论和组合优化概念完成的。该方法的适用性通过大规模测试案例进行了精心验证，其中将一组多周期，与目的相关的OD转换为现实的活动计划，该计划能够纳入超过99％的输入旅行需求。准确而详细的结果表明，所提出的方法具有巨大的潜力，可支持对人员出行行为进行全面分析。与目的相关的OD被转换为现实的活动计划，能够纳入超过99％的输入旅行需求。准确而详细的结果表明，所提出的方法具有巨大的潜力，可支持对人员出行行为进行全面分析。与目的相关的OD被转换为现实的活动计划，能够纳入超过99％的输入旅行需求。准确而详细的结果表明，所提出的方法具有巨大的潜力，可支持对人员出行行为进行全面分析。