Jason Hawkins, Omid Armantalab
arXiv 28 Jun 2026 · Econometrics
arXiv:2606.29145 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kung, Franki Y.H. and Scholer, Abigail A (2020) The pursuit of multiple goals | 1.000 | 5 | 3 | 100% |
| 2 | Swait, Joffre and Marley, A.A.J (2013) Probabilistic choice (models) as a result of balancing multiple goals | 0.909 | 8 | 4 | 75% |
| 3 | Marley, A.A.J. and Swait, J (2017) Goal-based models for discrete choice analysis | 0.855 | 8 | 3 | 62% |
| 4 | Swait, Joffre and Franceschinis, Cristiano and Thiene, Mara (2020) Antecedent Volition and Spatial Effects: Can Multiple Goal Pursuit Mitigate Distance Decay? | 0.855 | 8 | 3 | 62% |
| 5 | Swait, Joffre and Argo, Jennifer and Li, Lianhua (2018) Modeling simultaneous multiple goal pursuit and adaptation in consumer choice | 0.843 | 3 | 3 | 100% |
| 6 | Hur, Taegyu and Allenby, Greg M (2022) A Choice Model of Utility Maximization and Regret Minimization | 0.763 | 6 | 2 | 67% |
| 7 | van Osselaer, Stijn M. J. and Janiszewski, Chris (2012) A Goal-Based Model of Product Evaluation and Choice | 0.737 | 3 | 2 | 100% |
| 8 | Habib, Khandker Nurul (2023) Rational inattention in discrete choice models: Estimable specifications of RI-multinomial logit (RI-MNL) and RI-nested logit (R… | 0.644 | 3 | 2 | 67% |
| 9 | Dellaert, Benedict G. C. and Swait, Joffre and Adamowicz, Wiktor L.… (2018) Individuals’ Decisions in the Presence of Multiple Goals | 0.644 | 2 | 2 | 100% |
| 10 | Emmons, Robert A (1986) Personal Strivings: An Approach to Personality and Subjective Weil-Being | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 109 scored citations.