Arrival Flights Research Articles

Analyzing tactical control strategies for aircraft arrivals at an airport using a queuing model

This paper proposes to analyze control strategies for arrival air traffic at an airport using a classical queuing model. The parameters of our model are estimated by means of a data-driven analysis of two years of radar tracks and flight plans for arrival flights at Tokyo International Airport from 2016 to 2017. Our results show that increasing the capacity with one or two more aircraft in the airspace up to 60 NM around the airport significantly mitigates arrival delays, even when assuming future, increased arrival traffic volumes. The outcomes of this study provide insights into the effectiveness of arrival control strategies and are seen as a means to recommend scenarios to be further analyzed with human-in-the-loop simulations.

Stochastic terminal flight arrival and departure scheduling problem under performance-based navigation environment

Flight arrival and departure scheduling (FAADS) problem, one of the critical tasks in terminal air traffic operation, faces new challenges as the terminal air traffic management has transformed to adapt to the performance-based navigation (PBN) environment; beside of that, the terminal system uncertainties which are usually due to the time-varying interference of the convective weather and flight arrival time will also exacerbate the difficulty to realize an efficient terminal air traffic operation. In order to effectively address the above issues, we propose the formulation and solution approach for a stochastic terminal FAADS problem under PBN environment. The proposed FAADS problem formulation combines the flexible 4-dimensional trajectory requirement under PBN environment and the stochastic quantifications both from the convective weather and flight arrival time uncertainties, which can finally make the FAADS results realize the avoidance of the convective weather and immunity of the flight arrival time variations within tolerable risk probabilities. We provide an efficient solution approach to tackle this problem with complex mixed integer and nonlinear programming basics and illustrate the capabilities of the solution approach by a test case on real terminal system in Shanghai Metroplex. Numerical results demonstrate the effectiveness of proposed problem formulation and solution approach.

A collision risk assessment method for runway threshold management: A case study of Singapore Changi Airport

Airports are indispensable infrastructures in an air transportation system with runways being the most critical component serving departures and arrivals. With constant increase in demand of air traffic, much effort has been made to manage the runway capacity to improve the throughput of airports. Apart from operational changes, there is a significant investment in runway infrastructure improvements such as new runway development/extension. However, many runways suffer from long runway thresholds due to safety constraints in face of approach path obstacles, which leads to reduction in Landing Distance Available (LDA), thereby reducing runway capacity. This paper proposes a method to manage the runway threshold by computing and assessing the collision risk of a given flight approach path with an obstacle profile. To do so, we develop the arrival flight profile along with the altitude distribution the landing aircraft using ADS-B data. We then factor in the height of obstacles with reference to the obstacle surface profile. The convolution of two distribution is then used to assess the collision risk between the aircraft on approach path and the obstacle for better management of runway threshold. The proposed model is applied to Singapore Changi Airport, which has a long runway threshold due to the ship movements in the Strait of Singapore, which are considered as safety risks to the landing aircraft. Results suggest that, for CAT I/II approaches, with aircraft having aerodrome reference code 3/4, the runway threshold for runway Singapore Changi Airport 20R can safely be reduced by approximately 100 meters, while meeting the safety requirements. This increases the Landing Distance available and may lead to an increase runway capacity.

Optimising flight connection times in airline bank structure through Simulated Annealing and Tabu Search algorithms

In hub and spoke airline networks, flight arrivals and departures generally have a bank structure to increase connections among spoke cities through a hub airport in order to provide cheaper service for higher volumes of air traffic. In this study, we introduce the airline bank optimisation problem with a novel mathematical model for improving flight connection times. The mathematical model aims to minimise the total connection times for transfer passengers and generates flight schedules regarding slot capacities in the hub airports. Since the problem is a combinatorial optimisation problem NP-hard and computational complexity increases rapidly for real-world problems, we employ the simulated annealing and the tabu search algorithms to achieve better solutions in a reasonable time. We generate sub-problems using real-world data and investigate the effectiveness of the algorithms. Finally, we present the results of a real case study of a Turkish airline company which has a hub airport connecting the flights between Middle Eastern and European cities.

Unimpeded Taxi-Time Prediction Based on the Node–Link Model

This paper presents a new method for predicting unimpeded taxi time based on the airport node–link model through statistical analysis of the airport surface detection equipment surveillance data. The proposed method can predict taxi-out and taxi-in times for departure and arrival flights, respectively, by calculating the link travel times on a node–link model of the airport surface movement. The prediction performance of the proposed method was evaluated using recorded track data from Incheon International Airport during April 2015. Furthermore, the prediction performance was compared with three other methods well known for calculating unimpeded taxi times. The root-mean-squared error of the unimpeded taxi times predicted by the proposed method showed a prediction error of about 1 min for the departure and arrival flights. Moreover, the results showed significant improvement in the predictions of the unimpeded taxi time compared to the other methods.

Using Fuzzy Evaluation Decision Model to Assess the Operation Scheme of Taxi in Shanghai Pudong Airport

In order to investigate the decision of the airport taxi driver whether to return to carry passengers, we established a fuzzy evaluation decision model based on the analytic hierarchy process, combined with the application analysis of Shanghai Pudong Airport. Based on the selection and quantification of decision indicators, we used the AHP method to calculate the weights to eliminate indicators and optimize the indicator system. Then, a two-level fuzzy evaluation model was established, and the Bayesian discriminant verification model was more reasonable. Finally, based on the data analysis of Shanghai Pudong Airport, combined with 16 different airport decision-making situations, the model was used to obtain the no-load return trips and the membership of waiting passengers. We compared and selected the larger membership value as the decision. Besides, the dependence of the model was analyzed, and it was found that the main dependent factors for decision-making were the number of flight arrivals and the driver’s arrival time. After solving the above, we provide relevant basis for the decision of airport taxi drivers.

Flight and passenger efficiency-fairness trade-off for ATFM delay assignment

This paper studies trade-offs between efficiency (performance) and fairness (equity), when assigning ATFM delay pre-tactically (on-ground at origin airport) due to reduced airport capacity at destination. Delay is assigned as the result of the optimisation of a deterministic multi-objective problem considering flight and passenger perspectives when defining objectives of performance and fairness. Two optimisation cases are presented: one where objectives are based on flight metrics, and another one where they are based on passenger metrics. The paper defines and analyses efficiency-fairness trade-offs: the concepts of price of fairness for flights and passengers are defined as the percentage of efficiency loss due to the consideration in the optimisation of fairness; whereas the price of efficiency is considered as the fairness loss relative to the maximum value of the fairness metric, when considering flight or passenger delay in the optimisation. The optimisation model is based on the ground holding problem and uses various objective functions. For performance, total delay for flights (considering reactionary delay), and total delay for passengers (considering outbound connections) are defined. For fairness, the deviation of flight arrivals from a Ration By Schedule solution, and the deviation of delay experienced by passengers with respect to the one obtained in an RBS situation are used. An illustrative application on traffic at Paris Charles de Gaulle airport, a busy European hub airport, and including realistic values of traffic is modelled. A comprehensive trade-off analysis is presented. Results show, how in some cases, gains on one stakeholder can be achieved without implying any detriment on the other one. Passengers are more sensitive to the optimisation and hence, their consideration when assigning delay is recommended. Further research should explore how to combine flight and passenger indicators in the optimisation and consider how the lack of data availability could be mitigated.

Differential evolution algorithm with elite archive and mutation strategies collaboration

This paper proposes a differential evolution algorithm with elite archive and mutation strategies collaboration (EASCDE), wherein two main improvements are presented. Firstly, an elite archive mechanism is introduced to make DE/rand/3 and DE/current-to-best/2 mutation strategies converge faster. Secondly, a mutation strategies collaboration mechanism is developed to tightly combine both strategies to balance global exploration and local exploitation. As a result, EASCDE can effectively keep population diversity in the early stage and significantly enhance convergence speed as well as solution quality in the later stage. The performance of EASCDE is verified by experimental analyses on the well-known test functions. The results demonstrate that EASCDE is superior to other compared competitors in terms of solution precision, convergence speed and stability. Moreover, EASCDE is also an efficient method in dealing with arrival flights scheduling problem.

A machine learning model to predict runway exit at Vienna airport

Runway utilisation is a function of actual yearly runway throughput and annual capacity. The aim of the analysis in this project is to find data driven prediction models based on the features and relevant scenarios that might impact runway utilisation. The Gradient Boosting machine learning method will be assessed on their forecast performance and computational time for predicting the procedural and non-procedural runway exit to be utilised after the landing rollout. The Gradient Boosting method obtained an accuracy of 79% and was used to observe key related precursors of unique data patterns. Tests were conducted using runway and final approach data consisting of 54,679 arrival flights at Vienna airport.

Predictors of Definitive Airway Sans Hypoxia/Hypotension on First Attempt (DASH-1A) Success in Traumatically Injured Patients Undergoing Prehospital Intubation

Background: Prehospital intubation success is routinely treated as a dichotomous outcome based on an endotracheal tube passing through vocal cords regardless of number of attempts or occurrence of hypoxia, or hypotension, which are associated with worse outcomes. We explore patient, provider, and procedure-related variables associated with successful definitive airway sans hypoxia/hypotension on first attempt (DASH-1A) in traumatically injured subjects undergoing endotracheal intubation at the scene of injury by a helicopter EMS system.Methods: This single-center retrospective chart review included patients with traumatic injuries and at least one attempted intubation by helicopter EMS at the scene of injury. Demographic and clinical variables were tested for association with DASH-1A and overall first-attempt success using univariate comparisons and multivariable logistic regression to produce adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Purposeful backwards stepwise elimination was used to develop logistic regression models for outcomes. Initial inclusion of covariates in multivariable models was based on clinical judgement, known or suspected risk factors and confounders for intubation success, and univariate associations.Results: Of 419 subjects screened, 263 met inclusion criteria. Median age was 34 years and the majority of subjects were Caucasian (95%), male (76%), and suffered blunt trauma (90%). The endotracheal tube was successfully placed on the first attempt in 198 (75.3%) of patients, but only 142 (55.3%) had a successful DASH-1A, and overall, 246 (94%) had an endotracheal tube passed successfully before hospital arrival. Factors significantly associated with successful DASH-1A were no ground EMS intubation attempt prior to arrival [aOR 2.2 (CI 1.0–4.9)], lack of airway secretions/blood [1.9 (1.0–3.4)], Cormack-Lehane Score of I and II [12.3 (4.5–33.2) & 3.2 (1.2–9.1), respectively], and bougie use [5.4 (1.8–15.8)]. For endotracheal tube passing only, the following were significantly associated with first pass success: grade of view I and II [aORs 87.3 (CI 25.8–295.7) & 6.8 (2.3–19.5), respectively], lack of secretions/blood [4.9 (2.1–11.2), bougie use [7.8 (2.3–26.3)], direct laryngoscopy [5.1 (1.5–17.0)] and not using apneic oxygenation through a nasal cannula [2.5 (1.1–5.6)].Conclusion: In our helicopter EMS system, successful endotracheal intubation on the first attempt and without an episode of hypoxia was associated with no ground EMS intubation attempt prior to flight crew arrival, lack of airway secretions/blood, Cormack-Lehane Score, and bougie use.

Research on the Problem of Sorting Parallel Runway Aircraft in Terminal Area Based on Genetic Algorithm

Serious air traffic congestion often occurs in busy airports and terminal areas, which seriously interferes with the normal operation of flights, resulting in a large number of flight delays, causing huge economic losses to airlines, and also the affecting the safe operation of flights in the terminal area. This study takes the two-runway airport as the research object, establishes the parallel runway arrival flight sequencing model and designs the double-encoded genetic algorithm, uses a pair of chromosomes to determine the runway allocation and landing sequence number of the arriving flight, and arranges the flight landing sequence and the runway. The simulation experiments were carried out on 10 flights that were about to land at a certain time in the airport, the experimental results show that the algorithm achieves flight sorting and runway allocation optimization, compared with the current FCFS (First Come First Served) algorithm, it can effectively reduce flight delay time and improve the utilization efficiency of the airport terminal area.

Development of a predictive model for on-time arrival flight of airliner by discovering correlation between flight and weather data

An important business of airlines is to get customer satisfaction. Due to bad weather, a mechanical reason, and the late arrival of the aircraft to the point of departure, flights delay and lead to customer dissatisfaction. A predictive model of on-time arrival flight is proposed with using flight data and weather data. The key research in this paper is to discover the correlation between flight data and weather data. The relation between pressure pattern and flight data of Peach Aviation, which is LCC (low-cost carrier) in Japan, are clarified, and it is found that the sea-level pressures of 3 weather observation spots, which are Wakkanai as the most northern spot, Minami-Torishima as the most eastern spot, and Yonagunijima as the most western spot, can classify the pressure patterns. As a result, on-time arrival fight is predicted at 77% of the accuracy with using Random Forest Classifier of machine learning. Furthermore, feasibility of the predictive model is evaluated by developing a tool of on-time arrival flight prediction.

Incremental-Learning-Based Unsupervised Anomaly Detection Algorithm for Terminal Airspace Operations

The terminal airspace system is one of the most complex human-made dynamical systems with a continuously evolving structure. Modern aviation technologies in the terminal airspace record detailed flight and airport information, including the states of aircraft and the operating conditions of airports as time series datasets, through on-board and ground-based systems. Detecting anomalies in such datasets is emerging as a key problem to understand air transportation system complexity and behavior. This paper proposes an incremental-learning-based anomaly detection algorithm that generates anomaly detection models from surveillance data. The algorithm is developed in a recursive fashion, such that it uses the overnight recorded data to improve the temporal-logic-based anomaly detection models daily—thus it is termed as an overnight update algorithm. The proposed algorithm is demonstrated with real air traffic surveillance data comprising of arrival flights to LaGuardia, John F. Kennedy, and Newark airports, thereby detecting anomalies for arrivals in the terminal airspace of the New York metroplex. This is followed by some analysis and insights from the results of these extensive tests.

Abstract 4: Predictors of Definitive Airway sans Hypoxia/Hypotension on First Attempt (DASH 1A) Success in Traumatically Injured Patients Undergoing Prehospital Intubation

Objective Prehospital intubation success is routinely treated as a dichotomous outcome based on an endotracheal tube passing through vocal cords regardless of number of attempts or occurrence of hypoxia, or hypotension, which are associated with worse outcomes. We explore patient, provider, and procedure-related variables associated with successful definitive airway sans hypoxia/hypotension on first attempt (DASH-1A) in traumatically injured subjects undergoing endotracheal intubation at the scene of injury by a helicopter EMS system. Methods This single-center retrospective chart review included patients with traumatic injuries and at least one attempted intubation by helicopter EMS at the scene of injury. Demographic and clinical variables were tested for association with DASH-1A and overall first-attempt success using univariate comparisons and multivariable logistic regression to produce adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Purposeful backwards stepwise elimination was used to develop logistic regression models for outcomes. Initial inclusion of covariates in multivariable models was based on clinical judgement, known or suspected risk factors and confounders for intubation success, and univariate associations. Results Of 419 subjects screened, 263 met inclusion criteria. Median age was 34 years and the majority of subjects were Caucasian (95%), male (76%), and suffered blunt trauma (90%). A total of 142 (55.3%) subjects had a successful DASH-1A airway, 198 (75%) had a successful first attempt non-DASH-1A airway, and overall, 246 (94%) had an endotracheal tube passed successfully before hospital arrival. Factors significantly associated with successful DASH-1A were no ground EMS intubation attempt prior to arrival (aOR 2.2), lack of airway secretions (1.9), Cormack-Lehane Score of I and II (12.3 & 3.2, respectively), and bougie use (5.4). For endotracheal tube passing only, the following were significantly associated with first pass success: grade of view I and II (87.3 & 6.8, respectively), lack of secretions (4.9), bougie use (7.8), direct laryngoscopy (5.1) and not using apneic oxygenation through a nasal cannula (2.5). Conclusions In our helicopter EMS system, successful endotracheal intubation on the first attempt and without an episode of hypoxia was associated with no ground EMS intubation attempt prior to flight crew arrival, lack of airway secretions, Cormack-Lehane Score, and bougie use.

Self-adaptive mutation differential evolution algorithm based on particle swarm optimization

Differential evolution (DE) is an effective evolutionary algorithm for global optimization, and widely applied to solve different optimization problems. However, the convergence speed of DE will be slower in the later stage of the evolution and it is more likely to get stuck at a local optimum. Moreover, the performance of DE is sensitive to its mutation strategies and control parameters. Therefore, a self-adaptive mutation differential evolution algorithm based on particle swarm optimization (DEPSO) is proposed to improve the optimization performance of DE. DEPSO can effectively utilize an improved DE/rand/1 mutation strategy with stronger global exploration ability and PSO mutation strategy with higher convergence ability. As a result, the population diversity can be maintained well in the early stage of the evolution, and the faster convergence speed can be obtained in the later stage of the evolution. The performance of the proposed DEPSO is evaluated on 30-dimensional and 100-dimensional functions. The experimental results indicate that DEPSO can significantly improve the global convergence performance of the conventional DE and thus avoid premature convergence, and its average performance is better than those of the conventional DE, PSO and the compared algorithms. Moreover, DEPSO is applied to solve arrival flights scheduling and the optimization results show that it can optimize the sequence and decrease the delay time.

FUEL PLANNING PROBLEMS FOR FLIGHTS, PLANNED VIA STANDARD ARRIVAL ROUTES (STAR) WITH DELAY LEGS

The article deals with the fuel planning problems for flights planned via standard arrival routes (STAR), with delay legs. Implementation of new standard arrival routes based on area navigation principles leads to increasing airspace capacity and reducing workload for both flight crews and air traffic controllers. More and more air navigation service providers implement modern STARs which include delay legs as their components. Delay legs are being used as modern alternative to delay actions performed with short time holding patterns or radar vectoring procedures. But, new STAR types’ implementation without changing fuel planning procedures has led to fuel consumption increase. The nature of problem is shown in the article with reference to recently designed, published and implemented Pulkovo airport new standard arrival routes with delay legs. The calculations made with the use of automated flight planning systems and shown extra fuel consumption are given. Contributing negative factors are described. Suggested methods of solving the problems allow avoiding extra fuel consumption and reducing pollution. The procedure for using the new approach to planning and performing flight via STARs with delay legs is described. Implementation of the new approach in arrival trajectory design, flight planning and flight performance via standard arrival routes with delay legs is actual for the existing arrival routes and the routes being projected.

MATHEMATICAL AND LOGICAL MODEL OF AIRCRAFT TIMETABLES

The article analyses the methods of forming the aircraft flight schedule based on the classical theory of schedules as a theory of multi-stage systems. It is shown that the timetable is a system without interruptions, since each specific flight at a certain point in time is carried out by only one aircraft. In accordance with the theory of schedules, the optimal distribution of a discrete finite set of requirements serviced by deterministic systems with one or more devices is analysed under various assumptions about the nature of their maintenance. It is shown that the weakest assumption regarding the abstract operator, which translates many requirements into many agreed and executed plans, is the monotonic and unimodal behaviour of the corresponding functional of the generated plans. The possibilities of applying the theory of multi-stage systems to create the optimal schedule for medium or large airlines are investigated. The basic requirements for constructing a schedule are advanced. Mathematical models of the schedule are constructed taking into account the basic data of the current movement of aircraft in the area of ​​the airport or air hub. It has been established that if the base airports change their purpose and act as terminal arrival airports, the corresponding matrix elements are inversely symmetric, and with a rarefied spatial distribution of airports, the matrix can tend to diagonally dominant. A comparative analysis of scheduling algorithms is performed (based on queuing models, based on lists with the distribution of flights over time, etc.). The most suitable indicators of the effectiveness of the formation of the optimal schedule and system of priorities were selected and justified, taking into account the advantages of the arrival and departure times of passenger flights. An expression is derived for the resulting functional schedule efficiency, with which you can get quantitative comparative assessments of the quality of the schedule.

Quantitative Analysis of Operational Efficiency for Arrival Flights Bound for Tokyo International Airport

Quantitative Analysis of Operational Efficiency for Arrival Flights Bound for Tokyo International Airport

The most reliable flight itinerary problem

Travel itineraries between many origin‐destination (OD) pairs can require multiple legs, such as several trains, shared rides or flights, to arrive at the final destination. Travelers expect transparent reliability information to help improve decision‐making for multi‐leg itineraries. We focus on airline travel and making a priori decisions about flight itineraries based on the reliability of arriving at the destination within the travel time budget. We model the reliability of multi‐leg itineraries and, given publicly available data, create probability distributions of flight arrival and departure times. We use these values in our reliability calculations for the entire itinerary. We implement a stochastic network search algorithm that finds the most reliable flight itinerary (MRFI). We also implement several ideas to improve the efficiency of this network search. Computational experiments help identify characteristics of the MRFI for a diverse set of OD pairs.

Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas

A popular phenomenon in the street-hailing taxi system is the imbalanced mobility services between city central and outside downtown areas, which leads to unmet demand outside downtown areas and competitions in city central areas. Understanding taxi drivers’ customer-searching behaviors is crucial to addressing the phenomenon and redistributing the taxi supply. However, the current literature ignores or simply models the taxi drivers’ behaviors, in particular, lacks the in-depth discussions on individuals’ heterogeneity. This study introduces the latent class model to identify the internal and external factors influencing the taxi drivers’ destination choice after the last drop-offs. Beyond the influencing factors, the modeling structure captures the heterogeneity in vacant taxicab drivers through introducing latent classes. The proposed model outperforms other discrete choice models, for instance, multinomial logit, nested logit, and mixed logit, based on the two study cases developed from the New York City yellow taxicab system. The empirical results first statistically indicate the existence of latent classes, which further empirically prove the heterogeneity in the choices by vacant taxicab drivers while searching customers. Moreover, we obtain a set of internal and external factors influencing the customer searching behaviors. For example, the taxicab drivers are sensitive to the demand at the search destination areas and the distance from the last drop-off location to the search destination areas and behave identically in particular under the conditions of high demand and short search distance. On the other hand, the external variables have different impacts on customer searching behaviors across the different groups of drivers in the both study cases, including peak hours, weekday, holiday, earned fare from last occupied trip, raining hours, and flight arrivals at airports. In final, the proposed modeling structure and findings are useful as a sub-model of taxi system modeling while developing strategies, as well as as a regional planning tool for taxi supply estimations.