Taxi Time Research Articles

The operational dynamics of end-around taxiways: Insights from Istanbul Airport

The rapid rise in global air traffic poses significant challenges for airports, particularly in maintaining operational safety and efficiency. One of the main issues is the risk of runway crossings during ground movements, which can lead to runway incursions. This is particularly problematic in airports with parallel runway operations, where managing multiple runways and ground movements is more complex. End-Around Taxiways (EATs) have been introduced as a strategic solution to mitigate these risks. However, their implementation also introduces new operational challenges and complexities. This study evaluates the impact of EATs on Istanbul Airport (LTFM), one of Europe’s busiest airports. Using Automatic Dependent Surveillance-Broadcast (ADS-B) data, the research examines the use of EATs in taxi times, acceleration counts and durations, stop-and-go counts, level-off times, hot spots, gate and stand usages by developing new catchment algorithms. The findings show that EATs present operational challenges such as increased taxi times, number of acceleration movements, and level off times. The study emphasizes the need to consider local airport configurations when implementing EATs and suggests new constraints to optimize their usage.

Performance Evaluation of Machine Learning Algorithms in Estimating Taxi Times at Istanbul Airport

Performance Evaluation of Machine Learning Algorithms in Estimating Taxi Times at Istanbul Airport

A novel bi-objective model for sustainable and efficient airport logistics management: A case study on Copenhagen airport

Due to the growing volume of air transportation and the scarcity of airport resources, aviation planners are paying more attention to the efficiency of airport resource management. Optimizing the assignment and scheduling of ground facilities plays an important role in reducing ground congestion, flight delays, and passenger dissatisfaction. To prevent any collisions among aircraft on the apron, taxi network, and runways, safety considerations should be strictly followed by traffic controllers. Minimizing taxi time is another matter of importance in airport logistics management that helps mitigate environmental concerns (e.g., fuel consumption, tire abrasion, and noise pollution). In this study, a bi-objective mixed-integer linear programming model is developed to optimize the ground resource scheduling problem, taking into account the challenges of gate assignment, taxi planning, and runway scheduling. The model is developed in a way that includes safety and sustainability considerations. The validation of the proposed model is investigated through several examples based on Copenhagen airport using the augmented ε-constraint method. Since the simultaneous scheduling of multiple resources is an NP-hard problem, two multi-objective metaheuristic algorithms are employed to deal with the large-scale problems. The performance of the algorithms is then compared in terms of coverage and convergence rates. Additionally, the results are discussed in detail to provide managerial implications for airport authorities.

Modeling and simulation study of end around taxiway operation of Shanghai Hongqiao Airport

Abstract With the rapid expansion of the aviation industry, an increasing number of Close Spaced Parallel Runway (CSPR) airports are either planning or constructing End Around Taxiways (EAT) to alleviate field operation pressures and enhance safety. Taking Shanghai Hongqiao Airport’s typical CSPR EAT configuration as a case study, this research integrates the airport’s current operational status with the anticipated requirements for future structural renovations and increased flight volumes. Various operational scenarios are established, and simulation research on optimising EAT operations is conducted in advance. The simulation study proceeds as follows: first, an AirTOP simulation model is constructed based on Hongqiao Airport’s actual operational construction. Subsequently, leveraging existing operational scenarios, five simulation scenarios are devised by activating EATs at the departure and approach ends of the eastern zone. The merits and drawbacks of these scenarios are thoroughly analysed. The findings indicate that, with escalating flight volumes, the utilisation of EAT for larger aircraft can curtail their holding duration by nearly 8 min, consequently reducing overall arrival holding duration by 6 min. Departures from gates proximate to T1 experience a 3-min reduction in holding duration through the adoption of EAT at the approach end. Despite an increase in taxi distance due to a higher proportion of aircraft taxiing around, the overall taxi time is diminished. Activating EATs at the departure and approach ends of the eastern zone effectively mitigates the adverse effects of heightened flight volumes on field operational efficiency.

Runway assignment optimisation model for Istanbul Airport considering multiple parallel runway operations

Abstract The aviation industry has rapidly developed in recent years. Due to the increased number of flight operations, managing air traffic has become essential. The air traffic management system aims to reduce the air traffic control workload and use existing resources more efficiently. This study proposed a new mixed integer linear programming model to minimise the total fuel consumption during taxi operations for the runway assignment problem, comparing the actual Istanbul Airport runway assignment data. The average taxi times are calculated using the 30,000-flight operations data for each arrival and departure taxi route. Also, 47 different aircraft types are obtained using the data for the fuel consumption calculation. The International Civil. Aviation Organisation (IACO) aircraft engine emissions databank provides the fuel consumption values for each aircraft according to engine type. This approach allows our model to calculate more realistic fuel consumption for taxi operations, as each aircraft engine type has a different fuel consumption value. The proposed model is implemented at Istanbul Airport, the busiest airport in Turkey, where multiple parallel runway operations are applied. The results showed that the proposed model reduced total fuel consumption for taxi operations between 6.6% and 14.4% compared to the actual Istanbul Airport runway assignment data.

Adopted LTO cycle to operational conditions at polish airports

The aim of this article is to take into account specifications of the airport area in LTO test, when impact of the air transport at the airport- proximate area is analysed. The LTO test doesn’t con-sider the specifications of e.g. airport manoeuvring area and length of taxiways. The LTO test calculation methodology requires knowledge of aircraft engine parameters, such as thrust, spe-cific fuel consumption and emission values indexes. Based on the data contained in the Engine Emissions Database (ICAO), the parameters of the LTO test can be clearly determined. The main analysis element was to conduct a taxiing simulation using the CKAS MotionSim5 Flight Simu-lator in order to indicate the operational taxiing time at individual polish airports. Statistically significant differences were found between the emissions of selected exhaust gas components in the regulation LTO test and the operational LTO test. Analyzes were carried out in relation to three different propulsions that are most often used by carriers serving the largest number of passengers in Poland.

Airport Ground Optimizer (AGO): A decision support system initiative for air traffic controllers with optimization and decision-aid algorithms

The growing global air traffic has necessitated more efficient ground management in airports, especially during peak hours. Current approaches mainly consist of radar-based systems and radio communications, which provide limited visual assistance. Hence, Decision Support Systems (DSS) are designed to assist human Air Traffic Controllers (ATCos) by providing reliable recommendations based on current data and situations. The Airport Ground Optimizer (AGO) is a DSS designed for ATCos to manage the complexities of ground traffic in airports. It is based on advanced optimization algorithms and intuitive user interfaces, offering an enhanced operational experience. AGO uses mixed-integer programming (AGO-MIP) and stochastic programming (AGO-STC) to detect conflicts based on expected gate release and taxi times, and then optimizes the entire schedule to minimize hold fuels, resolve conflicts, and reduce fuel consumption, emissions, and delay times. Its key strength lies in translating complex mathematical solutions into user-friendly visual representations. AGO's core functionality includes comprehensive visualizations such as position charts, delay graphs, and potential conflict maps, providing a clear, real-time picture of ground operations for informed decision-making. AGO's queue and gate occupancy analysis calculates and visualizes the maximum queue length and concurrent number of aircraft at gates, enhancing airport capacity, reducing aircraft waiting times, and streamlining ground traffic flow. Simulated in a high-traffic Turkish airport layout, AGO-MIP demonstrated significant improvement in operational efficiency compared to traditional First Come First Served (FCFS) approach. AGO outperformed the traditional FCFS approach, reducing hold fuel by 27.9%, cutting delay time by 21.6%, lowering HC, CO, and NOx emissions by 16.4%, 22.5%, and 29.3% respectively, and decreasing the maximum queue length and its duration by 15.3% and 26.6%, as well as decreasing the number of delayed aircraft by 4.8%. The AGO-STC is also tested using pushback release uncertainties in the case airport, providing robust and feasible sequences, clear feedback for all possible scenarios, and detailed outcomes for each scenario. The study concludes by discussing potential developments and current issues of the tool in detail.

An Adaptive Similar Scenario Matching Method for Predicting Aircraft Taxiing Time

Accurate prediction of taxiing time is important in ensuring efficient and safe operations on the airport surface. It helps improve ground operation efficiency, reduce fuel waste, and improve carbon emissions at the airport. In actual operations, taxiing time is influenced by various factors, including a large number of categorical features. However, few previous studies have focused on selecting such features. Additionally, traditional taxiing time prediction methods are often black-box models that only provide a single prediction result; they fail to provide effective practical references for controllers. Therefore, this paper analyses the features that affect taxiing time from different data types and forms a taxi feature set consisting of nine key features. We also propose a taxiing time prediction method based on adaptive scenario matching rules. This process classifies the scenarios into multiple typical historical scenario sets and adaptively matches the current target scenario to a typical scenario set based on quantified rules. Then, based on the matching results, a pre-trained model obtained from the corresponding scenario set is used to predict the taxiing time of an aircraft in the target scenario, aiming to mitigate the impact of data heterogeneity on prediction results. Experimental results show that compared to baseline methods, the mean absolute error and root mean square error of the proposed method decreased by 4.8% and 12.6%, respectively. This method significantly reduces the fluctuations in results caused by sample heterogeneity and enhances controllers’ acceptance of prediction results from the model. It can be used to further improve auxiliary decision making systems and enhance the precise control capabilities of airport surface operations.

Multi-Task Dynamic Spatio-Temporal Graph Attention Network: A Variable Taxi Time Prediction Model for Airport Surface Operation

Variable taxi time prediction is the core of the Airport Collaborative Decision Making (A-CDM) system. An accurate taxi time prediction contributes to enhancing airport operational efficiency, safety and predictability. The deep dynamic spatio-temporal correlation inherent in airport traffic data is critical for taxi time prediction. However, existing machine learning (deep learning) methods have been unable to thoroughly exploit these correlations. To address this issue, we propose a deep learning-based model called the multi-task dynamic spatio-temporal graph attention network (MT-DSTGAN). Our model also predicts future entire airport traffic flow and taxiing segment traffic flow as auxiliary tasks, with the goal of enhancing the accuracy of aircrafts’ taxi time prediction. The proposed MT-DSTGAN model is implemented and assessed through a case study of Beijing Capital International Airport with a real-world dataset. The advantage of the proposed model, which shows better performance in various evaluation metrics, is demonstrated in a comparative study with other baseline works. In summary, the proposed MT-DSTGAN exhibits promising capabilities in perceiving the dynamic changes in the taxiing process of aircraft and demonstrates the ability to capture complex spatio-temporal correlations in airport traffic data.

Investigation of emissions from passenger flights Denizli Çardak Airport, Türkiye

AbstractDue to developing aviation sector, number of aircraft in the world is increasing. Along with this development, problems such as the decrease in air quality in and around the airport also arise. In this study, it is tried to calculate pollutant emissions occurring in 2022 during the LTO cycles of Denizli Çardak Airport in Turkey. These calculations are based on the information obtained from ICAO Engine Emission Data Bank and flight information published by the General Directorate of State Airports Authority (GDSAA). As a result of the data obtained, 74.64 ton/year pollutants (NOx-37.148 t/y, CO-35.398 t/y and HC-2.094 t/y) were calculated for 2022 at Denizli Çardak Airport. Of all emissions, NOx accounted for 50%, CO 47% and HC 3%. In the LTO cycle, the most fuel is burned in taxi cycle and pollutant emissions produced in this cycle are greater. With a 2 min reduction in taxi time, there will be an approximate 6.8% reduction in the total emission rate in the LTO cycle. Similarly, with a 4 min reduction in taxi time, there will be a 13.72% reduction in the whole emission rate in the LTO cycle. Unlike other studies, in this study the emission rates of various engines were compared. It has been calculated that the amount of pollutant emissions produced by the new generation Boeing 737 MAX LEAP-1B powered aircraft in LTO cycle is 25% less than the amount of pollutant emissions produced by the Airbus A320 NEO LEAP-1 A powered aircraft. The biggest factor here is that the emission of CO pollutants is less. Considering the emission rates produced by these four different engines (B737-800 CFM56-7B, A320 V2500-A1, B737 MAX LEAP-1B, A320 NEO LEAP-1 A), the Airbus A320 V2500-A1 engine is a more environmentally friendly engine than the other engines.

Hub Airport End-Around Taxiway Construction Planning Development: A Review

Hub airports typically have multiple parallel runways, requiring aircraft to transfer between them. This increases the risk of runway incursions. End-around taxiways (EATs) mitigate such risk by enabling bypassing without runway crossings. This review summarizes 15 EAT layouts worldwide and presents two classification methods: by configuration (N-type, M-type, Large N-type) and by operational relationship with runways (back-around, runway end-around, start-around). The key benefits of EATs were manifold, including the reduction of runway incursions, controller workload, and delays while improving communication safety, capacity, and efficiency. However, drawbacks such as increased land use, construction costs, and potentially longer taxi times and distances should be considered. Therefore, the optimization of the EAT layout is essential. In pursuit of optimal design, considerations should encompass limiting obstacle surfaces, flight procedures, navigation/lighting facilities, jet blast, and visual obstructions from end-around aircraft to departing aircraft. Notably, challenges arise in reducing distances between EATs and thresholds/ends. The given solutions include displacing thresholds, creating “sunken” lowered elevation EATs, and utilizing terrain shielding instead of metallic visual screens. A case study was introduced to demonstrate the successful improvement of the EAT via sunken EATs and terrain shielding at Guiyang Airport. The conclusion encourages further optimization of EAT layouts that balance safety and efficiency.

Quasi-Stochastic Departure Metering Model Considering Non-Deterministic Taxi Time, Standard Instrument Departure Track Time, and a New Delay–Conflict Relationship

This paper presents a quasi-stochastic mixed integer programming model to determine the optimal flight departure schedule and sequence at airports. The proposed formulation addresses two key gaps in the existing departure metering strategies: (i) the variability and effects of the standard instrument departure (SID) procedure and (ii) the prospect of en route conflicts because of departure delays. The variability and effects of the SID procedure are captured through a non-deterministic parameter defined as the SID track time. The probability of en route conflicts because of departure delay is estimated based on a new delay–conflict relationship. Data used for implementation and validation of the model was collected from operations on Runway 7 of Chennai International Airport, India. Overall results indicate that the proposed departure metering model can act as a pre-emptive departure delay strategy framework that takes care of the SID procedure and en route conflict possibility with no harm to the runway throughput. The implications of this work hint toward better management of the airport operations and the performance of the overall air traffic management system.

A CNN-GRU Hybrid Model for Predicting Airport Departure Taxiing Time

Scene taxiing time is an important indicator for assessing the operational efficiency of airports as well as green airports, and it is also a fundamental parameter in flight regularity statistics. The accurate prediction of taxiing time can help decision makers to further optimize flight pushback sequences and improve airport operational efficiency while increasing flight punctuality. In this paper, we propose a hybrid deep learning model for departure taxiing time prediction based on the new influence factors of taxiing time. Taking Pudong International Airport as the research object, after analyzing the scene operation mode, we construct the origin–destination pairs (ODPs) with stand groups and runways and then propose two structure-related factors, corridor departure flow and departure flow proportion of ODP, as the new features. Based on the new feature set, we construct a departure taxiing dataset for training the prediction model. Then, a departure taxiing time prediction model based on convolutional neural networks (CNNs) and gated recurrent units (GRUs) is proposed, which uses a CNN model to extract the high-dimensional features from the taxiing data and then inputs them to a GRU model for taxiing time prediction. Finally, we conduct a series of comparison experiments on the historical taxiing dataset of Pudong Airport. The prediction results show that the proposed hybrid prediction model has the best performances compared with other deep learning models, and the proposed structure-related features have high correlations with departure taxiing time. The prediction results of taxiing time for different ODPs also verify the generalizability of the proposed model.

More Electric Aircraft Conversion to All-Electric During Ground Operations: Battery-Powered Landing Gear Drive System

Raising awareness about environmental issues moves the aerospace industry towards electrification and the corresponding solutions are already present at some airports. However, commercial aircraft are the missing links in claiming the all-electric ground operations. They rely on fossil fuels without any electric alternative due to the technological inability to store large amounts of energy while maintaining low weight of batteries. The issue diminishes if an electric system uses only a fraction of energy normally consumed by the engines and comprises kinetic energy recovery. Accordingly, this paper demonstrates the landing gear drive system for a narrowbody air-plane which has the sustainable and economic means to replace all onboard engines throughout ground operations. The system is simulated in MATLAB/Simulink and leads to the kinematic results which are based on the real drive cycles. The kinematics are subsequently used to estimate the overall on-ground power and energy demand of a more electric aircraft (MEA). The impact is maximized with the components scaled according to performance metrics and two-speed gear ratio optimization. The net fuel advantage is demonstrated for different ground operation modes, taxi times and flight path lengths.

Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology

Airport arrival and departure movements are characterized by high dynamism, stochasticity, and uncertainty. Therefore, it is of paramount importance to predict and analyze surface taxi time accurately and scientifically. This paper conducts a comprehensive review of existing studies on surface taxi time prediction and analysis. Firstly, the overall research framework of surface taxi time prediction and analysis is categorized from three perspectives: taxi time type, movement type, and modeling method. Then, focusing on the two means of taxi time analytical modeling and simulation modeling, the existing mainstream models and methods are categorized, and the main ideas and scope of application of the various methods are analyzed. Finally, the paper presents the future development direction of surface taxi time prediction prospects. The research results are aimed at providing basic support and methodological guidance for reducing the uncertainty in airport surface operation and enhancing the level of control and decision-making ability of airport surface operation.

Airport energy conservation and runway configuration

Efficient ground operation of aircraft directly impacts airport's courses of energy conservation and green operation. Typical runway configurations used in large airport were classified and analyzed. Combining with flight ground operation, 2 methods of comparison and selection of carbon emission were proposed, which were based on taxiing distance and simulation respectively. In addition, above 2 methods were applied in large-sized airport runways configuration selection and taxiways optimization. Application results show that the method based on taxiing distance suits for rough estimation in the early stage of airport planning, the method based on simulation considering both ground taxiing and delay time suits for a more detailed study on airport planning.

Spatial and Temporal Characteristics Analysis and Demand Forecasting based on ARIMA Model - An Example of Yellow Taxi in New York

With the development of technology and urbanization, cars have greatly facilitated people's travel, but they also bring problems such as road congestion, pollution and accidents, which restrict the healthy development of cities. How to predict traffic flow efficiently and accurately has become a hot topic. Taxi data is highly recommended in urban transportation research due to the characteristics of broad spatial coverage, long driving time and relatively complete data. In this paper, the data of yellow taxis in New York during March 2023 are used to present the demand changes by means of line chart thermal map, and divide the database of March into two parts, one is training set and the other one is test set, then simulation and analysis using ARIMA model. It reveals the changing relationship between taxi demand and time. By predicting the traffic flow, this study can alleviate the congestion of urban main roads and improve the quality of citizens’ life. At the same time, forecasting commuting demand can balance supply and demand, provide more efficient services for operating companies, taxi drivers and passengers, and provide decision-making basis for government planning.

Delay in the Air or Detour on the Ground?—A Case Study in Guangzhou Baiyun International Airport

Collaboration between terminal airspace and airport surface operation shows an increasing significance for the best efficiency of both parts of the air traffic management domain. Runways play a critical role in connecting the two parts for departure and arrival aircraft. Suppose the gate and the entry fix of an aircraft are predetermined according to the flight plan, and they are on the opposite side of the airport terminal. The aircraft will either spend more time (i.e., delay in the air) landing on a runway close to its gate or take a longer distance (i.e., detour on the ground) taxiing to its gate if a runway close to its entry fix is assigned. This paper proposes a runway assignment model considering terminal airspace operation and airport surface movement simultaneously to discover how runway assignments can affect integrated operations. Four different runway assignment schemes are applied in this model. Subsequently, a metaheuristic method is proposed to solve the model. Furthermore, the historical taxiing and flight time data are analyzed to demonstrate the potential benefits of runway reassignment. Finally, the results show that the free assignment of the runway stands out among the four schemes, not only in the performance of terminal airspace operation (lower flight time) but also in airport surface movement (lower pushback delay, taxi time).

A Conflict Resolution Strategy at a Taxiway Intersection by Combining a Monte Carlo Tree Search with Prior Knowledge

With the escalating complexity of surface operations at large airports, the conflict risk for aircraft taxiing has correspondingly increased. Usually, the Air Traffic Controllers (ATCOs) generate route, speed and holding instructions to resolve conflicts. In this paper, we introduce a conflict resolution framework that incorporates prior knowledge by integrating a Multi-Layer Perceptron (MLP) neural network into the Monte Carlo Tree Search (MCTS) approach. The neural network is trained to learn the allocation strategy for waiting time extracted from actual aircraft taxiing trajectory data. Subsequently, the action probability distribution generated with the neural network is embedded into the MCTS algorithm as a heuristic evaluation function to guide the search process in finding the optimal conflict resolution strategy. Experimental results show that the average conflict resolution rate is 96.8% in different conflict scenarios, and the taxiing time required to resolve conflicts is reduced by an average of 42.77% compared to the taxiing time in actual airport surface operations.

Evaluation of the Trade-Off between Ground Delays and Intersecting Departures under Various Pilot Acceptance Rate Scenarios

The increasing demand for air traffic at airports necessitates the efficient utilization of ground facilities such as runways and taxiways. Intersecting departures, in which one or more aircraft take off from intersecting points on the runway, is a commonly used approach to increase runway capacity and reduce ground delays and taxi times, as well as noise and air pollution. However, the procedure carries potential risks such as runway incursion and excursion. This creates a trade-off between minimizing the number of intersecting departures and minimizing ground delays. In practice, the decision to perform an intersecting departure is ultimately up to the pilot, resulting in uncertainty in the acceptance rate of these types of takeoffs. In this study, a departure sequencing model was developed for a single-runway airport that considers intersecting departures and various pilot acceptance rate scenarios. The primary objective of the model is to minimize total ground delay, including taxi delays, runway holds, and conflict holds. The secondary objective is to minimize the number of intersecting departures by directing the most operationally critical aircraft to the intersection takeoff. The epsilon constraint method—a multi-objective scalarization method—was used to reveal the trade-offs between the objective functions. The results of the model were compared with a traditional scenario that only allows take offs from the beginning of the runway. As a result, average delay savings ranged from 17.1% to 31.5% in various acceptance rate scenarios, as well as average taxi time savings ranging from 4.9% to 8.4% compared with the traditional scenario.