Modelling changes in travel behaviour mechanisms through a high-order hidden Markov model

Integrating complicated travel behaviour mechanisms into transportation studies is necessary for understanding and modelling urban mobility. However, insufficient research has been conducted in this direction, especially when travellers make decisions using different mechanisms. This study develops a data-driven framework to model day-to-day route choice dynamics, in which different interpretable travel decision-making mechanisms and efficient model training algorithms are incorporated. The route choice is estimated following a Dirichlet distribution. By introducing a high-order hidden Markov state model, the framework can detect the routine and sudden changes of the mechanism and apply them accordingly for prediction. We propose a particle-based Markov chain Monte Carlo algorithm to estimate model parameters. As a pioneering work that links transportation data with different behaviour mechanisms, we demonstrate the feasibility of the proposed framework through a numerical example. With more transportation data, the proposed approach could become an attractive alternative to conventional transportation models.