Generation and Validation of Driver Personas Using Local Language Models for Multi-Agent Traffic Microsimulation Supporting Sustainable Transport Planning

The assessment of sustainable transport measures relies on traffic microsimulation, in which safety, travel time, energy consumption, and emissions emerge from individual driver behavior. Driver differences can be described using personas: brief verbal characterizations provided to the decision-making model. The text must retain the specified characteristics and influence the model’s responses accordingly. This paper presents a method for generating and evaluating such descriptions for multi-agent traffic microsimulation with local language models running on a single consumer-grade graphics card. The scope is limited to generating and validating the persona texts; the microsimulation itself and comparison with real drivers were outside this study. Four properties were evaluated: fidelity to the driver’s characteristics, text diversity, linguistic correctness, and impact on decisions, with the language-model judges compared against two human raters. The study involved 300 agents, 8 local models, 2 languages, 6 instruction versions, and 86,760 overtaking decisions. All models retained the specified features, but higher fidelity was usually accompanied by lower diversity, and error rates varied more than 100-fold across models. Personas differentiated the responses according to the specified risk propensity (ρ ≈ 0.90) but did not outperform a single labeled sentence. The method provides a validated, controllable representation of driver heterogeneity for microsimulation tools that assess the safety, efficiency, and environmental impacts of transport solutions.