Towards an integrated luti model of long term and short-term mobilitye decisions of households using social learning

This paper presents a model of long-term household mobility decisions that was developed in the context of the PUMA model (an agent-based integrated model of land use and transportation). It extends that state-of-the-art in that it integrates households’ relocation decision in the allocation of monetary and temporal resources on the household level. It interacts with a micro-simulation model of daily activity patterns in order to improve the representation of accessibility effects in LUTI modelling. The model uses a social learning algorithm to represent households’ decision making in a large state space under limited information. The model is illustrated in a small scale numerical example.