Abstract
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} = 0.99) for all the workers, and the activity sequence pattern home–education–home is rather well predicted ({mathrm{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.
Highlights
Since decades, urban travel forecasting models have played a crucial role in supporting infrastructure investment decisions
The current study modeled activity sequence patterns at the person level because the methodology used for model building can provide better results at the person level than at the household level as the unit of analysis
Each question is considered as a variable and each answer is considered as a category of that variable (Husson et al 2017)
Summary
Urban travel forecasting models have played a crucial role in supporting infrastructure investment decisions. He applied cluster analysis to identify five travel-activity patterns He used a linear logit model to analyse a contingency table to examine the relationship between activity sequences and socio-demographic variables. The current study develops a methodology that considers complete activity sequence patterns of individuals based on their socio-demographic attributes. The current study modeled activity sequence patterns at the person level because the methodology used for model building can provide better results at the person level than at the household level as the unit of analysis. For household type definition in trip generation, it is state of practice to use 30 survey records as a threshold for each household type to be considered (Moeckel et al 2017) This criterion was used in the current study and 112 activity sequence patterns were found with survey records of 30 and more. The contribution of each variable to the multiple correspondence analysis dimensions can give an idea of the most important categories which explains variability in the dataset
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