Abstract
We define metrics to quantify the level of overall delay and propose an agent-based data-driven model with four factors, including aircraft rotation, flight connectivity, scheduling process, and disturbance, to build a simulator for reproducing the delay propagation in aviation networks. We then measure the impact on the propagation by the delay at each airport and analyze the relevance to its temporal characteristics. When delay occurs, airline schedule planning may become infeasible, and rescheduling of flights is usually required to maintain the function of the system, so we then develop an improved genetic algorithm (GA) to reschedule flights and to relax the root delay. Results indicate that priority-based strategy rather than First-Come-First-Serve can achieve minimum overall delay when congestion occurs, and aircraft rotation is the most important internal factor contributing to delay propagation. Furthermore, the reschedule generated by the improved GA can decrease delay propagation more significantly compared to the agent-based model.
Highlights
Air transportation has become the main means of travel connecting di erent countries and di erent races.[1,2,3,4,5] Further, it has overtaken rail transportation to become the more popular method used by travelers, and it has made many global pursuits much more accessible, such as resource allocation,[6] the forecasting of epidemics,[7] the optimization of transportation systems,[8] and disaster response.[9]Air transportation systems have been traditionally expressed as graphs with vertices and edges representing airports and ights, respectively
At the end of this study, we discuss two rescheduling strategies to guide the design of controlling strategies for e ectively reducing ight delay propagation
We rst propose an agent-based data-driven model focusing on four factors, including aircraft rotation, ight connectivity, scheduling process, and disturbance, to create a simulator for reproducing the delay propagation in aviation networks
Summary
Air transportation has become the main means of travel connecting di erent countries and di erent races.[1,2,3,4,5] Further, it has overtaken rail transportation to become the more popular method used by travelers, and it has made many global pursuits much more accessible, such as resource allocation,[6] the forecasting of epidemics,[7] the optimization of transportation systems,[8] and disaster response.[9]. Though most of the above work succeeded in modeling and decreasing delay propagation in daily operations,[35–40] and most existing studies have found many factors which a ect ight delays, it is not yet clear on how ight delays spread; it is di cult to accurately describe the mechanisms of delay propagation in the aviation network considering the airlines’ dependence and all the internal and external factors in the system. Compared to the current rescheduling strategy from real data, we nd that it is possible to achieve much less overall delay with the new rescheduling strategy obtained by our algorithm
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