Airport taxi situation awareness with a macroscopic distribution network analysis

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: aircraft taxi time indices (ATTIs), surface instantaneous flow indices (SIFIs), surface cumulative flow indices (SCFIs), aircraft queue length indices (AQLIs), and slot resource demand indices (SRDIs). 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 ATTIs, SCFIs and 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

Scheduling and Airport Taxiway Path Planning Under Uncertainty

Congestion and uncertainty on the airport surface are major constraints to the available capacity of the air transport system. This project is to study the problem of planning and scheduling airport surface movement at large airports. Specifically, we focus on the departure time scheduling and taxiway path planning of multiple aircraft under uncertainty. We also developed a simulation tool that is capable of simulating aircraft movement along the taxiway and possible uncertainty during the movement

Benefits of Additional Runway Crossings on Parallel Runway Operations

As the air transportation industry expands, airports face numerous challenges to manage the increasing traffic. Among these problems, runway crossings are a considerable source of ground traffic inefficiency and risk. Building end-around taxiways are the only strategy to avoid crossings, but these are not always feasible, and therefore airport planners must find alternatives. This study consisted of a simulation over an airport that currently requires a vast amount of its arrivals to go through runway crossings in order to reach the apron; the airport simulation software utilized was the Total Airspace and Airport Modeler (TAAM). The process began with a thorough validation of a baseline model against the historical data of the airport, followed by the design and simulation of three alternatives, which had one, two, and three runway crossings subsequently added. The simulation also included two flight schedules resembling the operations of 2016 and 2026, in order to forecast the impact of the additional crossings in the upcoming years. Finally, an analysis with ANOVAs and t-tests of the simulation outputs revealed significant decreases in arrival and departure taxi times, along with no significant changes in runway or sequencing delay

Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

This study aims to develop a controllers decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILP-based optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA)

Optimizing Integrated Arrival, Departure and Surface Operations Under Uncertainty

In airports and surrounding terminal airspaces, the integration of arrival, departure and surface scheduling and routing have the potential to improve the operations efficiency. Recent research had developed mixed-integer-linear programming algorithm-based scheduler for integrated arrival and departure operations in the presence of uncertainty. This paper extends to the surface previous research performed by the authors to integrate taxiway and runway operations. The developed algorithm is capable of computing optimal aircraft schedules and routings that reflects the integration of air and ground operations. A preliminary study case is conducted for a set of thirteen aircraft evolving in a model of the Los Angeles International airport and surrounding terminal areas. Using historical data, a representative traffic scenario is constructed and probabilistic distributions of pushback delay and arrival gate delay are obtained. To assess the benefits of optimization, a First- Come-First-Serve algorithm approach comparison is realized. Evaluation results demonstrate that the optimization can help identifying runway sequencing and schedule that reduce gate waiting time without increasing average taxi times

Improved situation awareness for autonomous taxiing through self-learning

As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs’ autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. Indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically

Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page

Real-Time Calculation and Adaption of Conflict-Free Aircraft Ground Trajectories

To improve the quality of airport surface operations and to pave the way for more autonomous systems, the calculation and adaptation of ground trajectories for aircraft is the backbone for every improvement. These trajectories should be conflict free and easily adaptable to changing conditions in real-time, but on the other hand optimized regarding configurable criteria. This paper describes how this can be achieved using artificial intelligence, especially a multiobjective A* algorithm coupled with a genetic algorithm. The genetic algorithm uses a flexible objective function that can be used to tune the resulting trajectories to the specific needs of the airport/air navigation service provider