Generating and understanding human daily activity sequences using Time-Varying Markov Chain models

Abstract

Human activity patterns have raised broad interests across fields and activity-based modeling has been applied to analyze travel behaviors, effects of policies, etc. In this paper, we examined daily activity sequences from large volumes of datasets collected in the Tokyo Metropolitan Area over thirty years. We show that, using a simple time-varying (inhomogeneous) Markov Chain model, activity sequences at the individual level can be generated with high accuracy. The predictive power of the model is comparable to neural network models when generating activity sequences for each administrative region. The results are surprising considering the huge number of potential activity sequences and the complicated process of activity planning. A fundamental relationship between time-varying Markov Chain models and Multinomial Logit models is recognized to understand the unexpected accuracy. A new mechanism is proposed on how people make daily activity plans from the perspective of cost and utility, where the probability of one plan being chosen is the same as the probability of it being generated by the Markov Chain model. From this perspective, the Markov process becomes a utility-based model. Furthermore, given the average time-varying transition probabilities over a large spatial and temporal scale, the Markov Chain model for any specific area is reconstructed with six parameters. The generation accuracy using the reconstructed model is examined. Moreover, the statistical characteristics of the costs assigned to the Markov Chain model are investigated.