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ICTD 2025

Artificial Intelligence (AI) in Transportation

POSTER RECEPTION (View list of posters here)

Artificial Intelligence in GIS for Transportation Applications and Challenges

Tuesday, June 10, 2025
4:45 PM – 6:00 PM MT
Abstract: Artificial intelligence (AI) and machine learning (ML) have transformed Geographic Information Systems (GIS), particularly within the transportation sector. This systematic review, conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, explores the evolving role of AI and ML in transportation-related GIS applications. A comprehensive review of scholarly databases (Science Direct, Scopus, TRR, TRID) was undertaken to identify studies focusing on the use of AI/ML for predictive modeling, traffic management, infrastructure monitoring, and transportation planning. The findings reveal the significant advantages of AI and ML in enhancing data accuracy, improving decision-making, and automating complex tasks within GIS. Despite these benefits, challenges remain, including data privacy concerns, model transparency, and the need for standardization. This review provides critical insights into the benefits and limitations of AI/ML in transportation GIS, offering recommendations for future research and policy development to maximize the potential of these technologies in shaping modern transportation systems. The study emphasizes the importance of developing robust data governance frameworks to address privacy issues. Furthermore, integrating AI/ML with existing GIS workflows can improve the efficiency of transportation management systems. The findings suggest that standardizing AI/ML practices across the industry could lead to more consistent results. Finally, collaboration between researchers, policymakers, and technology developers is essential to realize the full potential of AI/ML in GIS.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Upon completion, participants will be able to uderstand the benefits and challenges of using AI/ML in transportation-related GIS applications.
  • Upon completion, participants will be able to recognize the potential for improving transportation management through the standardization and integration of AI/ML techniques within existing GIS workflows.
  • Upon completion, participants will be able to identify the implications of data privacy, model transparency, and standardization for AI/ML integration in GIS.