Taxi Time Research Articles

A Decision Support Tool for the Pushback Rate Control of Airport Departures

Airport surface congestion control has the potential to mitigate the increase in taxi times and fuel burn at major airports. One possible class of congestion control strategies predicts the departure throughput, and recommends a rate at which to release aircraft pushbacks from the gate. This paper describes the field-testing of these types of strategies at Boston Logan International Airport, focusing on the communication of the suggested rate to the air traffic controller, and additional support for its implementation. Two Android tablet computers were used for the field-tests; one to input the data and the other to display the recommended rate to the air traffic controllers. Two potential decision-support displays were tested: a rate control display that only presented a color-coded suggested pushback rate and a volume control display that provided additional support to the controllers on the number of aircraft that had called-ready and had been released. A survey of controllers showed that they had found the decision-support tool easy to use, especially the additional functionality that is provided by the aircraft volume control display. The field tests were also found to yield significant operational benefits showing that such a congestion control strategy could be effective in practice.

Using Ant Algorithm to Arrange Taxiway Sequencing in Airport

Over the last decade, air traffic flow at airports experienced an increase never seen before. Whereas several improvements have been achieved in enlarging the en-route traffic capacity, little has been done in order to decrease congestion on the airport surface. Aircraft taxiing is an expensive process, responsible for causing delays for both passengers and airlines and pollution problems. This paper presents an optimization solution for aircraft taxi-scheduling problem using Ant Colony Optimization, a method for search and optimization inspired by the behavior of real ants in nature. The simulations presented were based on real data from Brasilia International Airport. However, the developed model can be used in any airport for optimal taxiing routes searching. The paper has shown the optimization of taxiing to be efficient in achieving minimization of aircraft taxi time and complies with security constraints at airports. Index terms – Ant algorithm, air traffic flow management, optimization, simulation, taxiway sequencing

Aircraft taxi time prediction: Comparisons and insights

The predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1min makes efficient and predictable ground operations at airports indispensable. Accurately predicting taxi times of arrivals and departures serves as an important key task for runway sequencing, gate assignment and ground movement itself. This research tests different statistical regression approaches and also various regression methods which fall into the realm of soft computing to more accurately predict taxi times. Historic data from two major European airports is utilised for cross-validation. Detailed comparisons show that a TSK fuzzy rule-based system outperformed the other approaches in terms of prediction accuracy. Insights from this approach are then presented, focusing on the analysis of taxi-in times, which is rarely discussed in literature. The aim of this research is to unleash the power of soft computing methods, in particular fuzzy rule-based systems, for taxi time prediction problems. Moreover, we aim to show that, although these methods have only been recently applied to airport problems, they present promising and potential features for such problems.

Modeling and designing aircraft taxiing patterns for a large airport

At large airports, aircraft takeoff and land simultaneously on multiple runways. As a result, taxiing is a complex and, at times, lengthy process. Planning for aircraft taxiing is an essential design element for the expansion of runways, taxiways, and terminals. Unnecessary taxiing should be reduced. In this study, we develop a taxiing model and calculate the taxiing time. We observed aircraft traffic patterns and listened to the communication of air traffic controllers (ATCs) in order to develop a taxiing model. The developed model was applied to Narita International Airport. We begin by estimating the time required for taxiing at Narita International for 2014. Next, we propose two strategies to reduce the taxiing time. The first strategy involves changes in departure times with controlled intervals between them. In this strategy, the average taxiing time is reduced by 16.5%. The other strategy requires adjustments in the time from the aircraft departure from the terminal to the start of taxiing. In this strategy, the maximum taxiing time is reduced by 11.5%, and the intervals between the terminal departures and taxiing were more uniform.

A combined statistical approach and ground movement model for improving taxi time estimations at airports

With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. Improved on-stand time predictions (for improved resource allocation at the stands) and take-off time predictions (for improved airport-airspace coordination) both require more accurate taxi time predictions, as do the increasingly sophisticated ground movement models which are being developed. Calibrating such models requires historic data showing how long aircraft will actually take to move around the airport, but recorded data usually includes significant delays due to contention between aircraft. This research was motivated by the need to both predict taxi times and to quantify and eliminate the effects of airport load from historic taxi time data, since delays and re-routing are usually explicitly considered in ground movement models. A prediction model is presented here that combines both airport layout and historic taxi time information within a multiple linear regression analysis, identifying the most relevant factors affecting the variability of taxi times for both arrivals and departures. The promising results for two different European hub airports are compared against previous results for US airports.

A more realistic approach for airport ground movement optimisation with stand holding

In addition to having to handle constantly increasing numbers of aircraft, modern airports also have to address a wide range of environmental regulations and requirements. As airports work closer and closer to their maximal possible capacity, the operations problems that need to be solved become more and more complex. This increasing level of complexity leads to a situation where the introduction of advanced decision support systems becomes more and more attractive. Such systems have the potential to improve efficient airside operations and to mitigate against the environmental impact of those operations. This paper addresses the problem of moving aircraft from one location within an airport to another as efficiently as possible in terms of time and fuel spent. The problem is often called the ground movement problem and the movements are usually from gate/stands to a runway or vice-versa. We introduce a new sequential graph based algorithm to address this problem. This approach has several advantages over previous approaches. It increases the realism of the modelling and it draws upon a recent methodology to more accurately estimate taxi times. The algorithm aims to absorb as much waiting time for delay as possible at the stand (with engines off) rather than out on the taxiways (with engines running). The impact of successfully achieving this aim is to reduce the environmental pollution. This approach has been tested using data from a European hub airport and it has demonstrated very promising results. We compare the performance of the algorithm against a lower bound on the taxi time and the limits to the amount of waiting time that can be absorbed at stand.

A Discrete Particle Swarm Optimization Algorithm for Gate and Runway Combinatorial Optimization Problem

In this study, we set the average taxi time of flight as the objective of the gate and runway assignment problem. We present a gate and runway combinatorial optimization model with several restrictions such as restrictions of gate and runway time, type of aircraft and service. We design a Discrete Particle Swarm Optimization (DPSO) algorithm to solve this problem. Inspired by the genetic algorithm and combined with the neighborhood search, we propose a new location update strategy. Finally, numerical experiments were carried out on two cases where gate supplication is adequate and it’s not, experimental results show that the discrete particle swarm algorithm achieved very good results.

The trade-off between taxi time and fuel consumption in airport ground movement

Environmental issues play an important role across many sectors. This is particularly the case in the air transportation industry. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and a key area of study is whether fuel burn and environmental impact improvements will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used.

Gate Assignment to Minimize Passenger Transit Time and Aircraft Taxi Time

This research focuses on airport gates, which are limited resources in the air transportation system. In spite of its importance, ramp congestion has not been considered in traditional research on gate assignment. This research, therefore, proposes a new perspective on the gate assignment problem that accounts for ramp congestion, as well as passenger flow. The proposed gate assignment problem minimizes weighted nominal taxi time and weighted taxi delay, as well as passenger transit time in the terminal. Nominal taxi time and taxi delay are weighted by the number of passengers on board. As a result, the flow in the passenger terminal and on ramps becomes efficient. To get near-optimal solutions within a reasonable amount of time, two metaheuristic algorithms, tabu search and genetic algorithm, are implemented for two ramp configurations. Also, the tradeoff between passenger flow and ramp congestion is analyzed.

A Collaborative Optimization Model for Ground Taxi Based on Aircraft Priority

Large hub airports have gradually become the “bottleneck” of the air transport network. To alleviate the “bottleneck” effect, optimizing the taxi scheduling is one of the solutions. This paper establishes a scheduling optimization model by introducing priority of aircraft under collaborative decision-making mechanism, and a genetic algorithm is designed to verify the scheduling model by simulating. Optimization results show that the reliability of the model and the adjusted genetic algorithm have a high efficiency. The taxiing time decreases by 2.26% when compared with an empirical method and the flights with higher priorities are assigned better taxi routes. It has great significance in reducing flight delays and cost of operation.

Calculation of HC, CO and NO<SUB align="right">x from civil aviation in Turkey in 2012

To meet the growing demand for air transport, airline companies doubled their aircraft numbers in Turkey, where civil aviation activities have shown great development in recent years. This increase in aircraft numbers resulted in an emission problem. Considering its impact on regional air quality, the growing emission amounts are at a non-negligible level. We especially take the busiest airports in Turkey into account, as these airports are generally located in widely populated areas. In this study, HC, CO, and NOx emission during the LTO for the selected five busiest airports were calculated by taking the 2012 domestic data of: 1) the General Directorate of State Airports Authority in Turkey; 2) EUROCONTROL’s average taxi time; 3) emission factors of ICAO engine datasheets. The calculated emission amounts are 215 tons/year, 1,483 tons/year, 1,417 tons/year, HC, CO, NOx respectively and the biggest emission factor is found to be as CO. Among the airports, Istanbul Ataturk Airport has the biggest polluting emission ratio.

Estimation of aircraft taxi fuel burn using flight data recorder archives

This paper builds a model for estimating the fuel consumption of a taxiing aircraft using flight data recorder information from operational aircraft. The taxi fuel burn is modeled as a linear function of several potential explanatory variables including the taxi time, number of stops, number of turns and number of acceleration events, and the coefficients are estimated using least-squares regression. The statistical significance of each potential factor is investigated. Our analysis shows that in addition to the taxi time, the number of acceleration events is a significant factor in determining taxi fuel consumption. Since the model parameters are estimated using data from operational aircraft, they provide more accurate estimates of fuel burn than methods that use idealized physical models of fuel consumption based on aircraft velocity profiles, or the baseline fuel consumption estimates provided by the International Civil Aviation Organization.

An approach to optimization of airport taxiway scheduling and traversal under uncertainty

Airport runways and taxiways have been identified as a key source of system-wide congestion and delay in the over-strained commercial air traffic system. To combat this growing problem, we present a novel approach for taxiway scheduling and traversal. Aircraft must traverse a taxiway, represented by a graph, from gates to their respective runways and arrive at their scheduled times while adhering to safety separation constraints. We describe a combinatorial mixed-integer linear program to determine the push-back time windows, aircraft speeds, stopping times, and in particular, traversal paths for a given graph and an imposed flight schedule as part of a single optimization problem. Safety and scheduling constraints are made robust to probabilistic deviations from the prescribed schedule and aircraft motion, and multiple objective functions are considered to examine the trade-off between taxi times and the probability of safety separation violation. Several scenarios are presented to demonstrate improvements gained from the method and possible uses for this approach.

Analysis of Taxiway Aircraft Traffic at George Bush Intercontinental Airport, Houston, Texas

Serving one of the largest metropolitan areas in the United States, the George Bush Intercontinental Airport (IAH) in Houston, Texas, is one of the 10 airports with the longest average taxi-out and taxi-in times. This paper assesses the congestion at IAH by analyzing taxi times and flight data during different hours of the day. The capacity of IAH is investigated by examining the number of departing flights on the ground. IAH is operating close to capacity most of the time. Because increasing airport capacity can mitigate congestion, this report develops a surface operation model based on analyzed results to achieve this aim. A mixed-integer programming formulation is proposed to optimize total taxi times by finding optimal taxi routes and the related schedules. The model is applied to a sample from real data.

Multi-Level and Multi-Objective Design Methodology of Taxiways for Large Airports

In this paper, the authors propose a method to expand taxiways at large airports that shortens taxiing time and reduces costs. To ease congestion at airports, expanding taxiways is needed. But the exorbitant cost of expansion dictates the discreet selection of expanded taxiways. Determining expanded taxiways is a multilevel and multi-objective optimization problem containing 1-0 integer problem and routing problem. However, little research on this topic is available. In this paper, the authors propose an algorithm to calculate Pareto solutions applying a Multi-Objective Genetic Algorithm (MOGA) and Ant Colony Optimization (ACO). The proposed method is twice as accurate as other methods, and solutions calculated by proposed method are available when flight schedules are disturbed

Optimization of Taxiway Routing and Runway Scheduling

This paper describes a mixed-integer linear programming optimization method for the coupled problems of airport taxiway routing and runway scheduling. The receding-horizon formulation and the use of iteration in the avoidance constraints allows the scalability of the baseline algorithm presented, with examples based on Heathrow Airport, London, U.K., which contains up to 240 aircraft. The results show that average taxi times can be reduced by half, compared with the first-come-first-served approach. The main advantage is shown with the departure aircraft flow. Comparative testing demonstrates that iteration reduces the computational demand of the required separation constraints while introducing no loss in performance.

Contributions of advanced taxi time calculation to airport operations efficiency

The current paper stresses the high priority of accurate taxi time calculation and suitable pre-departure management strategies in the context of Airport Collaborative Decision Making (Airport CDM) and contributes to an improved overall understanding of the associated requirements. Based on data analysis performed at Frankfurt Airport this paper compares results of static and advanced taxi time calculation. Contrarily to static calculations, where look-up tables for stand-runway-relations are used, advanced taxi time calculation takes into account the current traffic situation and calculates taxi times dynamically. The paper shows that the taxi time uncertainties when using static data lead to only 42% of flights within the accuracy of ± 2 minutes according to the requirements of the Airport CDM Implementation Manual for short term predictions (maximum 30 minutes before off-block). The application of advanced taxi time calculation leads to significant improvements with 66% of flights within the accuracy of ± 2 minutes at the moment when the aircraft leaves the stand. Hence, app. 50% more predictions are compliant with the Airport CDM requirements compared to static data. Based on these results, the paper assesses the effects of improved taxi time predictability onto airport operation efficiency with particular focus set to taxi time reductions due to minimized queuing at the departure runway. It is shown that advanced taxi time calculation contributes to a reduction of delays, fuel burn, CO2 emissions and costs for airspace users compared to the use of static look-up tables. However, it is also emphasized that the shifting of delays from the runway holding positions to the stand does not necessarily contribute to efficiency improvements. It is recommended that the pre-departure sequencing strategy must be chosen cautiously in dependence on the fuel and time costs for airlines as well the amount of departure delay that has to be absorbed.

Study on Optimization for Taxiway Routing Arrangement Based on Simulation

The proper arrangement of taxiways is one of the most important jobs for airport operations. Based on simulation technique, the problem that latency time was hard to be presented by mathematics analytic expressions is solved in this paper. By transforming operating limitations into constraint conditions, taxiway optimization model aims at the shortest weighting taxiing time is established and the genetic algorithm is used in solving the model. To improve the efficiency, proper deletions of scheme set are conducted by the rules. At last, the validity of the simulation model and the algorithm are illustrated by an example.

Gate Wait for Better Air

Commercial jets may spend as much as 30% of their total flight time taxiing on the tarmac before takeoff.1 In the United States, this “taxi-out time” translates into 6 million metric tons of carbon dioxide, 45,000 metric tons of carbon monoxide, 8,000 metric tons of nitrogen oxides, and 4,000 metric tons of hydrocarbons emitted into the atmosphere yearly.1 Holding planes at the gate for fewer than 5 extra minutes may be a simple way to reduce these emissions, according to a pilot study conducted at Boston Logan International Airport by researchers at the Massachusetts Institute of Technology (MIT).2 The MIT team studied departure data from Logan and developed models to minimize runway congestion by increasing the amount of time planes spent at the gate. Working with the Federal Aviation Administration (FAA) and air traffic controllers, the MIT team ran eight 4-hour tests in August and September 2010 during Logan’s busiest arrival and departure times. The average taxi-out time at Logan is about 20 minutes. Holding 247 flights at the gate for an average of 4.3 extra minutes reduced taxiing time by an average of 20%, and fuel consumption dropped by 16–20 gallons per plane. Takeoff times were not delayed, because once planes pushed back they proceeded quickly to takeoff. About 18 hours of taxi-out time were eliminated over the course of the study, resulting in an overall estimated fuel savings of 3,900–4,900 gallons. This simple strategy offers a potential win–win situation for airports, airlines, and neighboring communities whose air is polluted by airport emissions. Air traffic controllers and aircraft pilots “were very positive and liked the fluid flow of aircraft on the ground instead of long queues,” says study leader Hamsa Balakrishnan. She’s working with the FAA to improve the method and test it at other congested airports. The team is also working on a model to estimate emissions avoided through the reduction in fuel consumption. Hazardous air pollutants measured at or near airports include nitrogen dioxide, polycyclic aromatic hydrocarbons, fine particles, carbonyls, and volatile organic compounds.3,4,5 These pollutants have been generally linked to cancer,6 heart attack,7 and type 1 diabetes.8 In one study, people living within 5 miles of airports were 1.5 times more likely than people living farther away to be admitted to hospitals for a variety of respiratory diseases.9 “It’s important that we minimize emissions,” says Lourdes Maurice, executive director for environment and energy at the FAA. Although it remains to be seen whether reduced taxi-out times at other airports will translate into significant fuel savings and emissions reductions, Maurice says, “The initial benefits look very good and we are continuing efforts with MIT.”

Airline pilot cosmic radiation and circadian disruption exposure assessment from logbooks and company records.

US commercial airline pilots, like all flight crew, are at increased risk for specific cancers, but the relation of these outcomes to specific air cabin exposures is unclear. Flight time or block (airborne plus taxi) time often substitutes for assessment of exposure to cosmic radiation. Our objectives were to develop methods to estimate exposures to cosmic radiation and circadian disruption for a study of chromosome aberrations in pilots and to describe workplace exposures for these pilots. Exposures were estimated for cosmic ionizing radiation and circadian disruption between August 1963 and March 2003 for 83 male pilots from a major US airline. Estimates were based on 523 387 individual flight segments in company records and pilot logbooks as well as summary records of hours flown from other sources. Exposure was estimated by calculation or imputation for all but 0.02% of the individual flight segments' block time. Exposures were estimated from questionnaire data for a comparison group of 51 male university faculty. Pilots flew a median of 7126 flight segments and 14 959 block hours for 27.8 years. In the final study year, a hypothetical pilot incurred an estimated median effective dose of 1.92 mSv (absorbed dose, 0.85 mGy) from cosmic radiation and crossed 362 time zones. This study pilot was possibly exposed to a moderate or large solar particle event a median of 6 times or once every 3.7 years of work. Work at the study airline and military flying were the two highest sources of pilot exposure for all metrics. An index of work during the standard sleep interval (SSI travel) also suggested potential chronic sleep disturbance in some pilots. For study airline flights, median segment radiation doses, time zones crossed, and SSI travel increased markedly from the 1990s to 2003 (P(trend) < 0.0001). Dose metrics were moderately correlated with records-based duration metrics (Spearman's r = 0.61-0.69). The methods developed provided an exposure profile of this group of US airline pilots, many of whom have been exposed to increasing cosmic radiation and circadian disruption from the 1990s through 2003. This assessment is likely to decrease exposure misclassification in health studies.