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This study aims to review how generative AI is integrated into transportation analytics and its impact on decision-making processes.

August 5, 2026InfrastructuresOpen Access

Generative AI in Transportation Analytics: Foundations, Deep Generative Models, and Practical Use Cases

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Systematic review identifies trends and challenges of generative AI in transportation analytics, suggesting actionable insights for the field.

This study aims to review how generative AI is integrated into transportation analytics and its impact on decision-making processes.
Systematic review of 142 empirical transportation studies from 2018 to August 2025.
Analysis of deep generative models and foundation models in transportation workflows.
Bibliometric analysis, topic modeling, and application mapping to identify trends and gaps.
Identified a shift from prediction-centric modeling to decision-aware transportation intelligence.
Highlighted trends in generative AI adoption and persistent limitations like scalability and trust.
Outlined a roadmap for responsible deployment of generative AI in safety-critical systems.

Babaee et al. (2026) studied this question.

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Systematic review identifies trends and challenges of generative AI in transportation analytics, suggesting actionable insights for the field.