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Literature suggests that preferences, as denoted by taste parameters andconsideration sets, may evolve over time in response to changes in demographicand situational variables, psychological, sociological and biologicalconstructs, and available alternatives and their attributes. However, existingrepresentations typically overlook the influence of past experiences on presentpreferences. This study develops, applies and tests a hidden Markov model witha discrete choice kernel to model and forecast the evolution of individualpreferences and behaviors over long-range forecasting horizons. The hiddenstates denote different preferences i.e. modes considered in the choice set,and sensitivity to level-of-service attributes. The evolutionary path of thosehidden states is hypothesized to be a first-order Markov process. The frameworkis applied to study the evolution of travel mode preferences, or modalitystyles, over time, in response to a major change in the public transportationsystem. We use longitudinal travel diary from Santiago, Chile. The datasetconsists of four one-week pseudo travel diaries collected before and after theintroduction of Transantiago, a complete redesign of the public transportationsystem in the city. Our model identifies four modality styles in thepopulation: drivers, bus users, bus-metro users, and auto-metro users. Themodality styles differ in terms of the travel modes that they consider andtheir sensitivity to level-of-service attributes. At the population level,there are significant shifts in the distribution of individuals across modalitystyles before and after the change in the system, but the distribution isrelatively stable in the periods after the change. Finally, a comparisonbetween the proposed dynamic framework and comparable static frameworks revealsdifferences in aggregate forecasts for different policy scenarios.

Modeling and Forecasting the Evolution of Preferences over Time: A Hidden Markov Model of Travel Behavior