Uncertainty quantification in profile Hidden Markov Models (pHMM)-based activity sequences characterization

Recently, Liu et al. (2015) proposed a method to characterize activity sequences stemming from activity-travel diaries. The framework is structured as follows: from an extracted set of activity sequences, (a) the occurrence probabilities of the different activities are determined as well as their sequential order for aligning the activity sequences. Then, (b) profile Hidden Markov Models (pHMM) are defined based on the previous output. This technique is interesting given the fact that it is also able to include the irregular activities and, as a result, their derived trips. In this context, thinking about integration with an agent-based micro-simulation model requires, as a preliminary step, an uncertainty quantification analysis in order to measure the variability of the outcome. This approach is all the more necessary when agent-based micro-simulation is used to predict mid- and long-term system states.