Project Summary

Air travel is a major contributor to global emissions, and inefficiencies at the airport—like extra taxi time and airborne holding—only exacerbate the problem. These inefficiencies also impact the bottom line, costing airlines billions annually in excess fuel consumption. To help mitigate these issues, the research team applied predictive modeling to increase the efficiency of airport operations.

In partnership with San Antonio International Airport (SAT), the team identified ways to address the airport's key sustainability challenges, including excess heat, noise, emissions, and fuel waste. Using SAT's operational data from a two-week period with nearly 1000 departure flights and over 700 arrival flights, the team first developed a machine learning model to predict airport operations. Predictions were then quantified by emissions and economic costs to better understand the sustainability and economic impacts of SAT's operational performance. Building on this analysis, the team developed a decision-making framework designed to optimize SAT's sustainability and efficiency goals by adjusting operational decisions in response to real-time airport information.

In addition to forging a strong partnership between the U-M and San Antonio International Airport, the team shared their findings and gathered feedback  from a range of US and global airport authorities and stakeholders. These included:

• San Francisco International Airport;
• Detroit Metropolitan Airport;
• Wayne County Airport authority;
• Air Transport Operations group at TU Delft in the Netherlands;
• Singapore Changi Airport Group;
• Participants at the US-Japan International Workshop on Sustainable Aviation in Japan. 

The team’s broad engagement with these organizations paved the way for future collaborations. Looking ahead, the team aims to leverage the strengthened partnerships and enhanced understanding of airport operations optimization gained through this catalyst grant by expanding the application of their prediction model to additional airports. They also plan to pursue further funding to investigate the behavioral and socioeconomic factors that shape how airport decision-makers establish sustainability goals.

This project received a $10,000 Catalyst Grant in 2023.

Project team: Wenbo Sun, PI (College of Engineering | U-M Ann Arbor); Max Z. Li, Co-I (College of Engineering | U-M Ann Arbor); Parth Vaishnav, Co-I (SEAS | U-M Ann Arbor).