Flight Parout: A simulation platform for intelligent flight path reroutes for adverse weather

Flight contingencies, such as adverse weather, may lead to changing aircraft's flight paths. The flight path rerouting process should be efficiently carried out to minimise costs while maintaining crew and passenger safety. In view of global aerospace targets of improving airspace and operational efficiencies, more research is required to develop efficient rerouting techniques and procedures. The increased availability of highly accurate global navigation satellite systems (GNSS) provides opportunities for improved flight navigation. The enhanced navigational accuracy is crucial to the adoption of performance-based navigation (PBN) by aircraft. The impact of these navigational improvements on flight rerouting and safety needs to be tested and investigated. A rerouting simulation and optimisation framework, called as Flight Parout, has been proposed in this paper to support the rapid development, testing and integration of new rerouting techniques. New algorithms can be easily added into the system. One of the advantages of this system is to test and compare the efficiency of different rerouting techniques in solving the proposed rerouting problems. Flight Parout integrates aeronautical, weather, flight and traffic information to optimise the rerouting process. The platform supports the acquisition of data from the System Wide Information Management (SWIM) data exchange infrastructure. In addition, the simulation framework can evaluate the impact of rerouting techniques on passengers. Passenger impact is measured by parameters such as flight delays, passenger inconvenience and missed connections. This framework/system can be deployed as advanced decision support tools for pilots and air traffic controllers. To handle the complex search space, genetic algorithm-based rerouting methods have been proposed and developed in this framework. One challenge with existing algorithms was that they got trapped in local optima. To solve this problem, the rerouting methods were enhanced by using an improved mutation technique. Results show that the proposed rerouting techniques were able to efficiently escape local optima. On the average, the proposed methods produced reroutes with lower costs, compared with those of alternative methods. Numerical results for en-route scenarios showed that the framework sufficiently optimised flight path reroutes to minimise costs. The results demonstrated that the proposed rerouting algorithms had better abilities for solution search. Compared with existing methods, derived reroutes had reduced impact on flight schedules and passengers. In addition, conflicts with surrounding air traffic were avoided by the reroutes generated using the platform.