Airport Taxi Situation Awareness with a Macroscopic Distribution Network Analysis

Abstract

This paper proposes a framework for airport taxi situation awareness to enhance the assessment of aircraft ground movements in complex airport surfaces. Through a macroscopic distribution network (MDN) of arrival and departure taxi processes in a spatial-temporal domain, we establish two sets of taxi situation indices (TSIs) from the perspectives of single aircraft and the whole network. These TSIs are characterized into five categories: taxi time indices, surface instantaneous flow indices, surface cumulative flow indices, aircraft queue length indices, and slot resource demand indices. The coverage of the TSIs system is discussed in detail based on the departure and arrival reference aircraft. A real-world case study of Shanghai Pudong airport demonstrates significant correlations among some of the proposed TSIs such as the aircraft taxi time indices (ATTIs), surface cumulative flow indices (SCFIs) and aircraft queue length indices (AQLIs). We identify the most crucial influencing factors of the taxi process and propose two new metrics to assess the taxi situation at the aircraft and network levels, by establishing taxi situation assessment models instead of using two systems of multiple TSIs. The findings can provide significant references to decision makers regarding airport ground movements for the purposes of air traffic scheduling and congestion control in complex airports.

Aircraft rerouting and rescheduling in multi-airport terminal area under disturbed conditions

“…sj ≥ εijωij(si+κij), εij, ωij∈{0,1}, si, sj, κij ≥ 0, ∀i, j∈F (3) sj ≥λijεijγij(si+φij), λij, εij, γij ∈{0,1}, si, sj, φij ≥ 0, ∀ i, j∈F (4) sj ≥ϑijεijγij(si+ϕij), ϑij, εij, γij ∈{0,1}, si, sj, ϕij ≥ 0, ∀i, j∈F (5) sj ≥λijεijςij(si+ψij), ψij = E/Vj, λij, εij, ςij ∈{0,1}, si, sj, E, Vj≥ 0, ∀i, j∈F (6) sj ≥λijεij (si+σi), λij, εij∈{0,1}, si, sj, σi ≥ 0, ∀i, j∈F (7) uj ≥ηijεij ρij (ui+ϱij), ηij, εij, ρij∈{0,1}, si, uj, ϱij ≥ 0, ∀i, j∈F (8) αi ≤ si ≤ βi, αi, βi ≥ 0, ∀i∈F (9) ( pi, qi ) ∈Υi, pi, qi ≥ 0, ∀i∈A (10) ui = min(wij), wij≥ri +xij, ui, wij, ri, xij ≥0, ∀i∈F, j∈Z…”

“…According the features of formulated models, various algorithms are designed to get the optimal or close-to-optimal solutions, such as A* algorithm, genetic algorithm [9], Lagrange decomposition, and machine learning algorithm which is popular in recent years. Overall, most of the existing studies focus on the dynamic routing and scheduling and performance analysis in a single airport [10], considering adverse weather [11], and other important activities. However, the impacts caused by lack of shared resources, such as runway system, arrival fixes and departure fixes, and also the interactions among multiple airports in such cases are rarely considered simultaneously.…”

Machine Learning Techniques for Taxi-out Time Prediction with a Macroscopic Network Topology

“…In order to calculate the features of our methods for taxi-out time prediction, we adopt a model of macroscopic network topology proposed in our previous work [14] to formulate the machine learning inputs. By this macroscopic network topology, it is expected that we can find a group of features, which will be used to determine the predictor variables for taxi-out time predictions later.…”

An Integer Linear Programming Formulation for Post-Departure Air Traffic Flow Management

“…Air traffic congestion has long been a pressing concern in the domain of air transportation industry as a result of the increasing complexity of airport network structure, its operational environment, and aircraft ground movement [1]. Such an issue further cuts across operational concerns on huge credit losses, poor air passenger satisfaction, low air quality, and increased emissions, among others.…”