🤖 AI Summary
This study addresses the limited theoretical grounding in contemporary traffic behavior modeling, which often relies heavily on data-driven AI predictions without adequately accounting for the motivational underpinnings of travel decisions. To bridge this gap, the paper introduces Goal Pursuit Theory (GPT) into transportation research for the first time, systematically incorporating multi-goal conflicts, context-dependent goal activation, and decision-making mechanisms across temporal scales. This approach transcends the constraints of traditional Random Utility Models (RUM) and Regret Minimization Models (RRM). By integrating hybrid choice modeling with matrix factorization techniques, the authors empirically demonstrate GPT’s superior explanatory power in contexts such as activity scheduling, vehicle ownership, and residential location choice, while also offering actionable implementation guidelines and benchmark data requirements.
📝 Abstract
Travel behavior and demand modeling seeks to understand the factors that motivate transportation decisions. At the same time, the field is increasingly adopting algorithmic and artificial intelligence (AI) tools that improve predictive accuracy, often at the cost of a grounding in hypothesis-based theory validation and behavioural explanation. In this discussion paper, we use goal pursuit theory (GPT) to illustrate why behavioral theory is a necessary complement to prediction in travel behavior research. Unlike random utility maximization (RUM) or close alternatives (e.g., random regret minimization (RRM)), GPT explicitly models how travelers (1) activate context-dependent goals (hedonic, gain, normative), (2) resolve conflicts between competing objectives, and (3) make sequential decisions across temporal scales. We demonstrate GPT's merits through three transport applications: activity scheduling (handling hierarchical goal structures), vehicle ownership (disentangling bundled mobility goals), and location choice (capturing latent goal interactions via matrix factorization). We provide actionable guidance for implementation, including: (a) hybrid choice model specifications linking goals to observable behaviors, (b) parallels to complementary behavioral theories from the transportation field, and (c) data requirements and comparative benchmarks against RUM/RRM models.