Transportation planning plays a vital role in developing efficient and sustainable transportation systems that meet the evolving needs of communities. As technology advances, the field of transportation planning can benefit from the use of generative artificial intelligence (AI) models to enhance decision-making processes. Generative AI models have the potential to significantly affect transportation planning by generating synthetic data, forecasting future scenarios, and optimizing decision-making, to name a few effects.

The objective of this research is to develop an initial framework for the use and application of generative AI models that will develop the following:

• Educational resources and guidelines to enhance the understanding and adoption of generative AI models among transportation planning professionals, policymakers, and researchers.
• Processes to identify possible data limitations and data quality to support the effective use of generative AI models in transportation planning.
• Methods to enhance the transparency and interpretability of generative AI models, facilitating model validation, and accountability in the decision-making process.
• Practices to identify and mitigate biases in generative AI models to ensure the equitable and just application of these models in transportation planning.
• Strategies to overcome scalability and implementation challenges associated with the integration of generative AI models into existing transportation planning processes.