Generative AI for Transportation Operations and Planning: Methods, Applications, and Governance

The transportation sector is undergoing a profound digital transformation as emerging technologies reshape how mobility systems are designed, operated, and optimized. Among these, Generative Artificial Intelligence (GenAI) has attracted significant attention for its ability to create data-driven models, generate synthetic scenarios, and support complex decision-making processes in day-to-day operations and medium-to-long-range planning.

In transportation operations, GenAI can enhance demand forecasting, service scheduling, incident response planning, work-zone and maintenance staging, freight and last mile logistics, and real-time network management through robust scenario libraries. In transportation planning, GenAI can expand alternatives analysis, improve corridor and network design, quantify distributional impacts, and stress test strategies under uncertainty, including climate and disruption scenarios. By producing diverse, policy-relevant scenarios and augmenting scarce data, GenAI can help agencies improve accessibility, reduce emissions, and allocate resources more effectively.

In the context of transportation and sustainable mobility, GenAI offers new opportunities for traffic forecasting, multimodal planning, infrastructure design, and climate-sensitive policy development. Its capacity to simulate diverse operating conditions and stakeholder behaviours can enhance resilience, reduce environmental impacts, and improve accessibility. However, the integration of GenAI into transportation raises critical questions regarding reliability, equity, ethics, and governance that require systematic academic investigation.

This Research Topic examines how GenAI can be responsibly integrated into transportation operations and planning workflows to advance sustainability objectives. We seek contributions that evaluate methodological rigor, data governance, and model validation; demonstrate applied use cases in agencies and operators; and assess ethical, equity, and regulatory implications in the context of real planning and operational decisions.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• Perspective
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: Generative Artificial Intelligence (GenAI), Transportation, Digital Transformation, Sustainable Mobility, Data Governance

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.