Abstract

In the regional multiairport system, the contradiction between the limited operating resources and the large flight flow is serious, and the flight delays can easily lead to the occurrence of unsafe events. This paper investigates the abnormal flight recovery method in regional multiairport system based on risk control. The focus is to reschedule arrival-departure flights in real time with minimized delay time and risk probability. In this study, the risk about terminal area control and scene operation was considered in the analysis of the risk control model (RCM), which includes six key risk points: airspace control, flight conflict, ground service, apron support, ground control, and taxiing conflict. The mathematical model on flight recovery was constructed to solve minimized delay time and risk probability with MSINS (multistart algorithm with intelligent neighborhood selection). The data of a typical regional multiairport system in China were selected for experimental verification in order to compare the RCM with the traditional recovery model (TRM). The experimental results show that first, there are some hidden dangers in the traditional recovery methods of flight delay. Flight conflict and apron support are the risk points that need to be controlled most in the multiairport system. Secondly, for the effective solution with the shortest delay time, the RCM can reduce the overall operation risk of the system, but the flight delay time is a little longer. For the effective solution with the lowest risk probability, RCM can reduce the risk of system operation and the delay time of flights at the same time. Therefore, RCM can improve the security level of the system during abnormal flight recovery and ensure or even improve the recovery efficiency.

Highlights

  • Regional multiairport system (RMAS) refers to the cooperative operation of two or more airport groups in a certain economic area, optimizing the allocation of space resources and improving the utilization rate of airspace resources, which involves resource sharing and competition among multiple airports

  • On the other hand, increasing the risk control of terminal area and scene operation in the process of abnormal flight recovery can improve the security level of the system. erefore, this paper considers the airspace control and flight conflict in the terminal area, as well as the ground support, apron support, ground control, and taxiing conflict in scene operation as the risk control points, to study the abnormal flight recovery method based on risk control

  • We consider the risk factors in the terminal area and scene operation, establish a recovery model of quantitative risk control, and select the operational data of typical RMAS to study the regional multiairport abnormal flight recovery method based on risk control

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Summary

Introduction

Regional multiairport system (RMAS) refers to the cooperative operation of two or more airport groups in a certain economic area, optimizing the allocation of space resources and improving the utilization rate of airspace resources, which involves resource sharing and competition among multiple airports. In order to reduce the limitation of optimization decision, Rosenberger et al [6], Petersen et al [10], Sinclair et al [11], and Zhang et al [12] added constraints such as airport capacity limit, flow balance, and flight section constraint They solved the problem of integrated recovery of abnormal flights combining the problem of crew recovery and the problem of passenger recovery. We consider the risk factors in the terminal area and scene operation, establish a recovery model of quantitative risk control, and select the operational data of typical RMAS to study the regional multiairport abnormal flight recovery method based on risk control. In the case of the traditional recovery method (TRM), this paper analyzes the risk points that need to be controlled most in the multiairport system under various delays so as to provide technical support for improving the recovery efficiency of abnormal flights and ensuring the safe operation of the airport.

The Mathematical Model
C 5 F 8 J 10
Algorithm Designing
Case Study and Result Analysis
Flight Data Preprocessing
Conclusions
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