Activity sequencing is a crucial component of disaggregate modeling approaches. This paper presents a methodology to analyse and predict activity sequence patterns for persons based on their socio-demographic attributes. The model is developed using household travel survey data from Germany. The presented method proposes an efficient approach to replace complex activity-scheduling modules in activity-based models. First, the paper describes a multiple correspondence analysis technique to identify the correlation between activity sequence patterns and socio-demographic attributes. Secondly, a probabilistic model is developed, which could predict likely activity sequence patterns for an agent based on the results of the multiple correspondence analysis. The model is predicting activity sequence patterns fairly accurately. For example, the activity sequence pattern home–work–home is well predicted ( $${\mathrm{R}}^{2}$$ R 2 = 0.99) for all the workers, and the activity sequence pattern home–education–home is rather well predicted ( $${\mathrm{R}}^{2}$$ R 2 = 0.90) for students. The model predicts the 112 most common activity sequence patterns reasonably well, which covers 72% of all activity sequence patterns observed.

中文翻译：

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微观活动序列生成：基于社会人口特征的多对应分析解释旅行行为

活动排序是分解建模方法的重要组成部分。本文提出了一种基于社会人口统计属性分析和预测人的活动序列模式的方法。该模型是使用德国的家庭旅行调查数据开发的。所提出的方法提出了一种有效的方法来替换基于活动的模型中复杂的活动调度模块。首先，本文描述了一种多重对应分析技术，以识别活动序列模式和社会人口统计属性之间的相关性。其次，开发了一个概率模型，该模型可以根据多重对应分析的结果预测代理可能的活动序列模式。该模型相当准确地预测活动序列模式。例如，活动序列模式 home-work-home 被很好地预测 ($${\mathrm{R}}^{2}$$ R 2 = 0.99) 对于所有工人，活动序列模式 home-education-home 是相当对学生的预测很好（$${\mathrm{R}}^{2}$$ R 2 = 0.90）。该模型相当好地预测了 112 个最常见的活动序列模式，涵盖了观察到的所有活动序列模式的 72%。