Abstract

Air traffic control (ATC) and air traffic management (ATM) operate across multiple timescales and decision layers, yet both are fundamentally shaped by uncertainty arising from weather, capacity disruptions, and human decision-making. In this talk, I present two complementary research projects, one focused on air traffic control (ATC) and the other on air traffic management (ATM), that together offer a unified perspective on how uncertainty can be explicitly modeled and managed across tactical ATC operations and strategic ATM planning. At the tactical ATC level, we study pathfinder operations during convective weather and develop a decision-theoretic framework that captures stochastic airspace availability, flight acceptance behavior, and pathfinder sequencing. We show that the proposed models yield rich insights into system behavior and inform the design of operational decision support tools. At the strategic ATM level, we address uncertainty in airport ground delay programs through a distributionally robust optimization framework that hedges against capacity mis-specification and demonstrates strong out-of-sample performance using data from the US National Airspace System. Together, these results show how explicit uncertainty modeling across ATC and ATM decision layers can improve robustness and operational performance.

Max is an Assistant Professor of Aerospace Engineering at the University of Michigan, Ann Arbor. He also has courtesy appointments in Civil and Environmental Engineering as well as Industrial and Operations Engineering. Max received his PhD in Aerospace Engineering from the Massachusetts Institute of Technology in 2021. He received his MSE in Systems Engineering and BSE in Electrical Engineering and Mathematics, both from the University of Pennsylvania, in 2018. Max’s research and teaching interests include air transportation systems, airport and airline operations, Advanced Air Mobility, networked systems, as well as optimization and control.

Abstract

We formulate a vacant ride-sourcing or taxi driver’s routing decision as an infinite-horizon semi-Markov decision process (SMDP) in a road network, where a driver decides which link to take at each node and transitions to a new node depending on the stochastic vehicle-passenger matching process. A driver’s decision is based on observable and unobservable states.

The modeler’s job is to jointly estimate an average driver’s parameterized utility function as well as the state transition function based on the sequence of observed states and actions. We establish the existence and uniqueness of a fixed point solution to the Bellman equation for the SMDP, which is needed for the maximum likelihood estimation of model parameters. We use parallel computing to speed up the estimation algorithm to be applicable to a case study in a large network.

The expected fare, expected operating cost and number of intersections in the urban area are found to be significant predictors of drivers’ routing decisions. Comparison with several base models suggest the advantage of considering multiple decision cycles, low discount rate (corresponding to a discount factor close to 1), and joint estimation of routing and matching parameters.

Song Gao is Professor of Civil and Environmental Engineering at the University of Massachusetts Amherst. Her research focuses on travel behavior and transportation system analysis, with applications in smart and shared mobility, transportation planning, and sustainable transportation systems, and has been funded by local, regional and federal government agencies and private foundations, including the Massachusetts Department of Transportation, National Science Foundation, FHWA, and APRA-E. Prior to joining UMass, Prof. Gao worked as a transportation engineer at Caliper Corporation. She is an Editorial Board Editor of Transportation Research Part B, and past Associate Editor of Transportation Science. She received her Ph.D. and M.S. in Transportation from MIT, and B.S. in Civil Engineering from Tsinghua University.