A framework to identify housing location patterns using profile Hidden Markov Models

The determination of comprehensive activity-travel patterns is important in the context of agent-based micro-simulation modelling. This paper presents an improved method based on profile Hidden Markov Models (pHMMs) able to include information related to the agents’ residential locations. As proposed in the framework of Liu et al. (2015), pHMMs only characterize activity-travel patterns from the activity sequences perspective. In this context, information related to the primary activity locations (e.g. home, work) is not available and, as a result, it cannot be extracted from the pHMMs themselves. With respect to this limitation, we propose to apply the framework of Liu et al. (2015) with an extension to include characterization of residential locations. Following the established guidelines, the activity sequences and their related residential locations are extracted from the activity-travel diaries in order to estimate the regularity of the activities as well as their sequential order. Subsequently, within each residential activity, we include a categorization at an aggregate level (provinces). The methodology is powerful as it characterizes any length of sequence, allowing the generation of unlimited agent plans with information about residential location. Regarding data collection, the activity-travel diaries are provided by the Belgian Household Daily Travel Survey (2010). The results obtained after the simulations indicate a good match between the predicted and observed residential locations at both the national and provincial levels.