Distributed Air Traffic Flow Mangement

Distributed Model Predictive Routing and Scheduling for Minimizing Network-wise En-Route and Airport Arrival Delay in Air Traffic Flow Management with an Eulerian-Lagrangian Flow Dynamic Model Research Project in Air Traffic Management Research Institute (ATMRI) A joint research institute with Civil Aviation Authority of Singapore (CAAS) and Nanyang Technological University (NTU) Project Homepage

Summary

The goal of this research is to develop a distributed model-predictive air flow management approach (in terms of flight routing and scheduling), which is applicable to a large-scale air traffic network, and can mitigate the network-wise en-route and airport arrival delay caused by reduction of link capacities during abnormal situations such as severe weather in some regions or airspace restrictions due to military operational needs.

Project Objectives

• To minimize the air traffic delay and enhance the air traffic system usage.

• To develop a distributed model-predictive air flow management approach.

• To provide a procedure, which translates optimal flow rate assignments to flight trajectories (i.e., departure times, speed and routes).

• To explore the optimal air traffic flow management and flight trajectories generation when uncertainties are introduced.

Research Topics

Distributed Scheduling Algorithm in Air Traffic Flow Management An extended Eulerian-Lagrangian flow model is proposed to describe the network dynamics with possibly different aircraft types A quadratic integer programming problem is formulated for delay reduction under constraints of limited link capacities along with possibility of flight rerouting and diversion A distributed approach and a heuristic algorithm is developed to reduce the computational complexity

Flight Trajectories Generation A model aims at optimizing flight plan for the traffic system, i.e. assign route and schedule for individual aircraft With considerations of Dispatching constraint, Sequence constraint, Separation constraint, Capacity constraint Speed constraint

Simulation Package Design Combined the ATFM, flight trajectories generation module with simulation platform Developed based on AirTOp, a real-time air traffic simulator

Collaborators

• Prof. Rong Su (PI), Dr. Nadeesha Sandamali, Dr. Qing Li, Dr. Sanjoy Modal.

Publications

1. Y. Zhang, R. Su, G. G. N. Sandamali, Y. Zhang, C. G. Cassandras and L. Xie. A Hierarchical Heuristic Approach for Solving Air Traffic Scheduling and Routing Problem with a Novel Air Traffic Model. IEEE Transactions on Intelligent Transportation Systems. Sep 2018.

2. G. G. N. Sandamali, R. Su and Y. Zhang. Flight Routing and Scheduling Under Departure and En Route Speed Uncertainty. IEEE Transactions on Intelligent Transportation Systems. Major Revision. Sep 2018.

3. Y. Zhang, R. Su, N. Sandamali, Y. Zhang and C. G. Cassandras. A Hierarchical Approach for Air Traffic Routing and Scheduling. 56th IEEE Conference on Decision and Control (CDC’17), 2017.

4. Y. Zhang, R. Su, Q. Li, C. G. Cassandras and L. Xie, Distributed flight routing and scheduling in air traffic flow management. IEEE Transactions on Intelligent Transportation Systems, 18(10), 2681-2692, 2017.

5. R. Su and Y. Zhang. A Software Tool for En-route Air Traffic Flow Management in Large Air Traffic Networks. TD-183-17, July 26, 2017.

6. N. Sandamali, R. Su, Y. Zhang and Q. Li. Flight Routing and Scheduling with Departure Uncertainties in Air Traffic Flow Management. 13th IEEE International Conference on Control & Automation (ICCA), 2017.

7. Y. Zhang, R. Su, Q. Li, C. G. Cassandras and L. Xie. Distributed Flight Routing and Scheduling in Air Traffic Flow Management. 55th IEEE Conference on Decision and Control (CDC’16), 2016.

8. Y. Zhang, Q. Li and R. Su. Sector-based Distributed Scheduling Strategy in Air Traffic Flow Management. 14th IFAC Symposium on Control in Transportation Systems (CTS’16), 2016.

9. Q. Li, Y. Zhang and R. Su. A Flow-based Flight Scheduler for En-route Air Traffic Management. 14th IFAC Symposium on Control in Transportation Systems (CTS’16), 2016.