Generalized Method of Modeling Minute-in-Trail Strategy for Air Traffic Flow Management

Abstract

With the rapidly increasing air traffic demand, the demand-capacity imbalance problem of sector is surfaced gradually. And, minute-in-trail/miles-in-trail (MIT) is an effective strategy to balance the traffic demands and capacity. In this work, we consider the MIT strategy generation problem for the situation that a sector with NC corridors is affected by convection weather for Timb time periods. Given the sector capacity Cwt, t=1,…,Timb, under convection weather, we propose a three-phase optimization framework to generate E-MIT strategy to achieve the demand-capacity balance. First, we take the sector capacity of Timb time periods under convection weather as a whole, that is, ∑t=1TimbCwt, and then a dynamical programming-based method is proposed to allocate ∑t=1TimbCwt for NC corridors such that the capacity resources Awi of each corridor CORi, i=1,…,NC, can be determined. Second, a 0-1 combination algorithm is used to allocate the capacity resources Awi into Timb time periods for each corridor CORi such that the candidate strategies set CSi of each corridor can be determined, where a strategy solji∈CSi is an array with Timb numbers and each number represents the maximum allowed number of flights entering into sector from CORi in one time period. Finally, a modified shortest path algorithm based on the backtracking method is taken to select the optimal strategy from CSi for NC corridors such that the total delay cost and air traffic control load are minimized. Additionally, a dynamical programming-based method is proposed to generate E-MIT strategy for the special case that the sector capacities of different time periods under convection weather are the same, that is, Cw1=Cw2=⋯=CwTimb, and the generated strategies of Timb time periods for a corridor are also the same. Experimental results show that compared with the proposed three-phase optimization method, rate-based method and need-based method will spend more 8.1% and 6.3% of delay cost, respectively. When considering the special case, the experimental results show that compared with the proposed dynamical programming-based method, the rate-based method and need-based method will spend more 10.2% and 7.5% of delay cost, respectively.