Modeling context-sensitive, dynamic activity travel behavior by linking short-and long-term responses to accumulated stress

As existing activity-based models of travel demand simulate activity travel patterns for a typical day, dynamic models simulate behavioral response to endogenous or exogenous change along various time horizons. Prior research predominantly addressed a specific kind of change, which usually affected a specific time horizon. In contrast, the current study aims to develop a dynamic model of activity travel decisions that links short-and long-term adaptation decisions in a hierarchical manner. Specifically, this study focuses on the bottom-up process of influence, in which problems with rescheduling on a daily basis may induce a long-term change. The authors assume that travelers will first explore short-term adjustments of their habitual activity travel patterns so as to cope with change and increasing stress. Only when travelers recognize that such adaptation strategies are ineffective will they consider long-term decisions. The proposed framework integrates three key concepts: aspiration, activation, and expected utility. Moreover, both rational and emotional mechanisms are taken into account. The study demonstrates model properties by using numerical simulation. Individual travelers are represented as agents, each with their cognition of the environment, habits, preferences, and aspirations. The results offer insight into the dynamics of traveler learning-adaptation and into the evolution of long-term decisions