A Multiobjective Optimization Approach for Reducing Air Traffic Collision Risk

Air transport contributes significantly to the globalization and world economic. With the increasing demand for both passengers and air cargo, future airspace may encounter unprecedented traffic pressure. It is always the paramount commitment of air transport to ensure flying safety. In the face of increasing traffic demand, it is pertinent to investigate how to reduce en-route collision risk without compromising the traffic demand. In this paper, we propose a multiobjective optimization based method to reduce the technical vertical risk (TVR) by controlling en-route air traffic speed. The suggested method simultaneously optimizes two objectives. The first one aims to minimize the TVR while the second tries to minimize the traffic delay. As the modeled optimization problem is non-convex and the two objectives conflict with each other, we therefore introduce two well-known multiobjective evolutionary algorithms named NSGA-II and NSGA-III and modify some of their operators to solve the proposed optimization problem. Finally, we carry out experiments on sixteen real-world daily traffic sample data that cover en-route flights within the Singapore flight information region (FIR). Experiments demonstrate that by optimizing the proposed problem using the introduced algorithms we obtain a set of speed control suggestions each of which can reduce the TVR for the Singapore FIR. This work will contribute both to strategical and tactical air traffic management as the aviation players can make the preferred choices based on the solutions yielded by the introduced algorithms.