Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes

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 (R-2 = 0.99) for all the workers, and the activity sequence pattern home-education-home is rather well predicted (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.

Automated summary

The paper introduces a methodology for predicting activity sequence patterns based on socio-demographic attributes and develops a model using household travel survey data from Germany. Utilizing multiple correspondence analysis and a probabilistic model, the model accurately predicts various activity sequence patterns, covering a significant portion of observed sequences.