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

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 corridors is affected by convection weather for time periods. Given the sector capacity , , under convection weather, we propose a three-phase optimization framework to generate E - strategy to achieve the demand-capacity balance. First, we take the sector capacity of time periods under convection weather as a whole, that is, , and then a dynamical programming-based method is proposed to allocate for corridors such that the capacity resources of each corridor , , can be determined. Second, a 0-1 combination algorithm is used to allocate the capacity resources into time periods for each corridor such that the candidate strategies set of each corridor can be determined, where a strategy is an array with numbers and each number represents the maximum allowed number of flights entering into sector from in one time period. Finally, a modified shortest path algorithm based on the backtracking method is taken to select the optimal strategy from for 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 - strategy for the special case that the sector capacities of different time periods under convection weather are the same, that is, , and the generated strategies of 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.