Arrival Operation Research Articles

Research on Runway Capacity Evaluation of General Aviation Airport Based on Runway Expansion System

To enhance the operational management capabilities of general aviation airports, this paper proposes a method for evaluating the runway capacity of general aviation airports based on the runway expansion system. Firstly, it provides a brief introduction to the flight rules of general aviation airports and arrival and departure flight procedures with symmetrical characteristics, which serve as a theoretical basis for establishing the runway expansion system. Subsequently, a runway expansion system that covers symmetrical flight activities such as departure and arrival under a visual flight rule and an instrument flight rule is proposed, providing a conceptual model for evaluating the runway capacity of general aviation airports. On this foundation, the classical space–time analysis model is improved to establish a single runway arrival, departure, and mixed operation capacity evaluation model for general aviation airports. Finally, the reliability and rationality of this method are verified through case evaluations and three sets of numerical experiments with symmetrical relationships. The experiments demonstrate that this method can better reflect the actual conditions of the runways at general aviation airports while ensuring flight safety, and it can provide a reference for related research.

A multi-objective fuzzy programming model for port tugboat scheduling based on the Stackelberg game

To solve the optimization problem of tugboat scheduling for assisting ships in entering and exiting ports in uncertain environments, this study investigates the impact of the decisions of tugboat operators and port dispatchers on tugboat scheduling under the scenario of dynamic task arrival and fuzzy tugboat operation time. Considering the features of the shortest distance tugboat principle, the first available tugboat principle, and the principle of fairness in the task volume of each tugboat, the tugboat company aims to minimize the total daily fuel consumption of tugboat operations, maximize the total buffer time of dynamic tasks, and minimize the total completion time as the objective functions. Due to the limitations of port vessel berthing and departure, as well as the allocation standards for piloting or relocating tugboats, the present study proposes a Stackelberg game-based fuzzy model for port tugboat scheduling with the tugboat operator and port dispatcher acting as decision makers at the upper and lower levels, respectively. A seagull optimization algorithm based on priority encoding and genetic operators is designed as a solution approach. CPLEX, genetic algorithm, standard seagull optimization algorithm, and simulated annealing algorithm are used to compare and analyze the solution results for the 45 problem cases generated from the actual data obtained from the Guangzhou Port. The results verify the efficiency of the proposed seagull optimization algorithm based on priority encoding and genetic operators. Furthermore, additional experiments are conducted to evaluate the changes in fairness coefficient, uncertain parameter correlation coefficients, and objective function correlation coefficients to demonstrate the practicality of the fuzzy programming model. This analysis involves adjusting the confidence level incrementally from 0 to 100% with respect to the model’s uncertain parameters.

Acoustic Assaults on the Auschwitz-Birkenau Concourse

Abstract This article investigates how sonic encounters shaped Jews’ experiences on the concourse at Auschwitz-Birkenau. By applying methods associated with sound and sensory studies, the article examines survivors’ memoirs and interviews to demonstrate that sonic violence was a key aspect of the genocide. The train ramp was a dynamic acoustic environment in which interlocking noises swelled and contracted as part of the arrival operation. As Jewish arrivals deboarded the trains, an array of threatening noises battered them, including amplified commands blasted in German, antisemitic slurs, vicious barking, beatings, and gunshots. SS guards used these acoustic assaults to perpetrate sonic violence against the arrivals, subduing their targets by instilling fear through sound. By generating an atmosphere of chaos and confusion through sound, they achieved their chief objective: prisoner compliance. Acoustic assaults inflicted somatic and psychological pain that manifested itself physically. Many deportees responded to the sensorial onslaught with their own sonorous outpourings. Cries, wails, and screams flooded the concourse, evidence of their trauma. Besieged sensorially, many became mentally immobilized, underlining the capacity of sonic violence to impair cognition. Their mental paralysis amid the cacophonous maelstrom marked their shattered subjectivities, which seared themselves sonically into their memories.

An Adaptive Dynamic Scheduling Policy for the Integrated Optimization Problem in Automated Transshipment Hubs

This study considers the multi-period dynamic integrated optimization problem in automated transshipment hubs, which includes berth allocation, quay crane assignment, and yard assignment problems. The planner needs to determine the berth and storage schedule in real-time. In the previous literature, the scholars always assume that all of the required information, especially the arrival time and operation time of vessels, is accessible prior to making any design decisions, and formulate the problem into an integrated model. However, the information of vessels is updated dynamically and changed frequently. Therefore, we consider a problem in a dynamic setting that decisions are made in each period and the scenario at each period depends on the decisions in previous periods. We formulate the problem into a multi-objective model, which aims to maximize total revenue, saved time deviation, saved transportation distance, and service quality. An efficient adaptive dynamic scheduling policy is developed, which adaptively balances the trade-offs between multiple objectives. Through numerical experiments, we demonstrate that our approach presents solutions with a delicate balance between multiple objectives and brings value to the automated container terminals compared to benchmark policies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Automated technologies have brought great opportunities for container terminals. While the construction of an automated container terminal requires enormous investment. Utilizing the resources of container terminals reasonably and efficiently can improve the operation efficiency and economic performance of automated container terminals. In practice, the scheduling of resources in container terminals always encounters uncertain factors. Therefore, this study proposes a dynamic integrated optimization policy for resources in automated container terminals to balance the trade-offs between multiple objectives given any pre-determined target in a dynamic environment.

The Optimization of Bus Departure Time Based on Uncertainty Theory—Taking No. 207 Bus Line of Nanchang City, China, as an Example

In order to optimize the bus departure time considering uncertain factors, this paper constructed an uncertain bi-level programming model for departure frequency and scheduling of a bus line. The uncertainty of passenger arrival and bus operation time were taken into account, combined with actual operation conditions. Nanchang 207 bus line was taken as an example to optimize the departure frequency and scheduling in the morning peak hour. The optimal departure frequency in the morning peak hour is 12 times. The overall index value of the route’s non-uniform scheduling during peak hours increased by 0.06 and 9.23% compared with uniform scheduling. The analysis results show that the effect of the non-uniform scheduling is obvious. The issue of bus line departure frequency and scheduling has a positive effect on improving the efficiency of public transportation, reducing operating costs and promoting the sustainable development of the public transportation system. This paper provides a theoretical support for bus operators to optimize route operations.

Establishing the Correlation Between Complexity and Performance for Arrival Operations

Air traffic complexity indicators play an essential role in measuring operational performance and controller workload. However, current studies mainly depend on the manual scoring method to scale performance or workload. This paper focuses on arrival operations and presents a data-driven strategy to establish the correlation between complexity and performance to avoid the subjectivity of the currently used manual scoring method. Firstly, we present twenty-six indicators for describing air traffic complexity and two indicators for arrival operational performance. Secondly, the clustering method distinguishes peak and off-peak situations for arrival operation. Moreover, clustering results are compared to investigate the correlation between complexity and performance initially. Thirdly, the classification method is adopted to determine such correlation further. In addition, we also identify the affecting factors which could influence operational performance. Finally, trajectories of arrival aircraft landing at Guangzhou Baiyun International Airport (ZGGG) are used for case validation. The results indicate that there is a strong correlation between complexity and performance. The accuracy and precision of classification are approximately 90%. Furthermore, the number of aircraft significantly impacts the arrival operational performance within TMA.

국내 표준계기도착절차(STAR)의 활용도 및 연속강하접근 운항 비율 분석

In response to the recent surge in aviation demand, major airport and aviation authorities continue to make efforts to formulate arrival procedures that take into account efficient aircraft separation, noise and environmental issues related to carbon (CO₂) emissions. In order to ensure efficient traffic control and environmental issues, as a result, a new concept Trombone, Point Merge, etc. have been introduced and widely used. However, these new concept incisions are becoming a factor that hinders operational efficiency and stability due to the restricted domestic airspace such as military airspace and excessive constraints of altitude, speed, etc. which do not reflect the concept of continuous descent operation and eventually needs to be modified to make continuous descent operation as feasible as possible. We herewith analyze and propose the way of improving flight safety and efficiency in the arrival operation procedure by supplementary modification which consequently contribute to the aviation industry international competitiveness.

Simulation analysis of applicant scheduling and processing alternatives at a military entrance processing station

Eligibility for enlistment into the US military is assessed by the United States Military Entrance Processing Command (USMEPCOM), an independent agency that reports to the Office of the Secretary of Defense (OSD) and not to any specific branch of military service. This research develops a discrete-event simulation for applicant processing operations at a Military Entrance Processing Station (MEPS) to investigate the viability of potential alternatives to the current applicant arrival and processing operation. Currently, all applicants arrive to the MEPS at the beginning of the processing day in a single batch. This research models and compares two alternatives with the status quo: split-shift processing, by which applicant arrivals occur in two batches: one at 06:00 and one at 11:00 and appointment-based processing, by which applicants may arrive during one of three, four, six, or eight appointment windows. Express-lane processing is also explored, in which applicants are allowed to bypass select processing stations. Experimental results indicate that split-shift processing is not viable under the current processing model due to an unacceptable decrease in applicant throughput. Results from appointment-based scenarios are mixed, with the critical factors being the time between appointment batches and their associated arrival times.

A data-driven method for flight time estimation based on air traffic pattern identification and prediction

Flight time estimation is expected to play a crucial role in predicting the Estimated Time of Arrival, which could help detect conflicts and manage arrivals. This paper proposes a novel data-driven method for flight time estimation based on arrival pattern identification and prediction. Firstly, a trajectory clustering algorithm is employed to group the arrival trajectories into different arrival patterns. A new trajectory representation technique is presented during the clustering process for better-describing arrival patterns. Secondly, we extract features from radar tracks for data-driven flight time estimation. These features consist of current states related, historical information related, traffic situation related, and environmental conditions related features. Furthermore, the permutation feature importance and recursive feature elimination method are adopted to reduce feature dimensions. Then, we develop three widely used tree-based models to estimate the flight time for each arrival pattern. We also propose an image-based flight patterns prediction method to classify each new arrival aircraft into the corresponding arrival pattern for actual operation. Finally, we take the Guangzhou arrival operation as a case to validate our proposed method. The results indicate that our proposed method could improve flight time estimating accuracy. Besides, through the data-driven strategy, we could also find several significant factors affecting the flight time within the Terminal Maneuvering Area.

Track Utilization Optimization Method for Arrival Yard of Marshalling Station Considering Arrival and Break-Up Coordination Operation

The efficiency of track utilization is one of the important factors for train operation in marshalling stations, and it can not only affect the turnover efficiency of trains, but also determine the capacity of railway stations. This paper presents an optimization method for track utilization in the arrival yard of a marshalling station considering Arrival and Break-up Coordination Operation (ABCO). The interaction between the push operation of trains and track utilization efficiency is investigated. First, the case of crossing the running route for shunting locomotives is analyzed. Whether the shunting route crosses or not can greatly affect the duration of trains occupying tracks in the station. Then, a nonlinear integer programming model (NLPM) is established based on the above analysis. The ultimate goal is to minimize the number of crossovers of the shunting route by adding a constraint condition for ensuring traffic safety. A track assignment algorithm based on ABCO for solving the NLPM is proposed. Train operations in the downlink arrival yard of station A are simulated with the proposed method. The results after applying the optimization method show that the average duration of trains on the tracks is reduced by 49.1%, and the degree to which tracks utilization is balanced is also significantly improved. The proposed method optimizes and extends the current achievements that have been reported in the literature.

Prediction of Arrival Flight Operation Strategies under Convective Weather Based on Trajectory Clustering

An airport’s terminal area is the bottleneck of the air transport system. Convective weather can seriously affect the normal flight status of arrival and departure flights. At present, pilots take different flight operation strategies to avoid convective weather based on onboard radar, visual information, adverse weather experience, etc. This paper studies trajectory clustering based on the OPTICS algorithm to obtain the arrival of typical flight routes in the terminal area. Based on weather information of the planned typical flight route and flight plan information, Random Forest (RF), K-nearest Neighbor KNN (KNN), and Support Vector Machines (SVM) algorithms were used for training and establishing the Arrival Flight Operation Strategy Prediction Model (AFOSPM). In this paper, case studies of historical arrival flights in the Guangzhou (ZGGG) and Wuhan (ZHHH) terminal area were carried out. The results show that trajectory clustering results based on the OPTICS algorithm can more accurately reflect the regular flight routes of arrival flights in a terminal area. Compared to KNN and SVM, the prediction accuracy of AFOSPM based on RF is better, reaching more than 88%. On this basis, six features—including 90% VIL, weather coverage, weather duration, planned route, max VIL, and planned Arrival Gate (AF)—were used as the input features for AFOSPM, which can effectively predict various arrival flight operation strategies. For the most frequently used arrival flight operation strategies under convective weather conditions—radar guidance, AF changing, and diversion strategy—the prediction accuracy of the ZGGG and ZHHH terminal areas can exceed 95%, 85%, and 80%, respectively.

Evaluation method of fault indicator status detection based on hierarchical clustering

In order to solve the evaluation problem of fault indicator arrival detection and operation detection, an evaluation method of fault indicator status detection based on hierarchical clustering is proposed. Firstly, the evaluation model for status detection of fault indicator in distribution network is established. Then, through the fault indicator state detection single result table and the fault indicator operation state detection result table, the single equipment evaluation of fault indicator status detection is described in detail. Secondly, through the fault indicator state detection manufacture result table and the fault indicator state detection manufacture evaluation value, the manufacturer equipment evaluation of fault indicator status detection is described in detail. Finally, through the fault indicator state detection itemized result table and fault indicator state detection itemized evaluation value, the itemized equipment evaluation of fault indicator status detection is described in detail. The case analysis shows that the method is effective and feasible, can meet the actual needs of the site, and has positive significance.

Data-Driven Method for the Prediction of Estimated Time of Arrival

Predicting the estimated time of arrival (ETA) plays an essential role in decision support (conflict detection, arrival sequencing, or trajectory optimization) for air traffic controllers. In this paper, a new multiple stages strategy for ETA prediction is proposed based on radar trajectories, including arrival pattern identification, arrival pattern classification, and flight time estimation. First, an intention-oriented trajectory clustering method is developed based on a new trajectory representation technique. Such a proposed trajectory clustering method can group trajectories into different arrival patterns in an efficient way. Second, an arrival pattern classification model is constructed based on random forest and XGBoost algorithms. Then, a flight time regression model is trained for each arrival pattern by using the XGBoost algorithm. Information on current states, historical states, and traffic situations is considered to build the feature set during these processes. Finally, the arrival operation toward Guangzhou International Airport is chosen as a case study. The results illustrate that the proposed method and feature engineering approach could improve the performance of ETA prediction. The proposed multiple stages strategy is superior to the single-model-based ETA prediction.

Benefits Derived from Arrival Management and Wake Turbulence Re-Categorization in China

Air traffic administration requires evidence when promoting new technology or a new concept of operation. Therefore, when decision support tools are applied, it is necessary to analyze the costs and benefits quantitatively. This paper focuses on the evaluation of Key Performance Indicators (KPIs) correlated with the improvement of arrival operations after the implementation of the Arrival Management (AMAN) system and Wake Turbulence Re-categorization in China (RECAT-CN). Firstly, we give an overview of the implementation of the AMAN system and RECAT in China. Secondly, the KPIs related to the arrival operation are established according to the characteristics of AMAN and RECAT-CN, based on the existing KPI systems in the field of Air Traffic Management (ATM). The proposed KPIs are: airport acceptance rate; final approach interval; flight time within the terminal area (TMA); and taxi-in time. Thirdly, arrival operation within the TMA around Guangzhou International Airport is used as an example to carry out the quantitative analysis. The region and time range were defined for the performance comparison, and external factors were also examined. Finally, using descriptive and inferential statistics, the proposed KPIs’ comparison results are presented and visualized. Such results indicate a significant improvement in arrival operation with the AMAN system and RECAT-CN at Guangzhou International Airport.

Approach Airspace Utilization Rate Mode of Terminal Area in Blocky Distributed Hazardous Weather

Hazardous weather has a great impact on the airspace operation of the terminal area, which will lead to a reduction in the utilization of the airspace of the arrival operation. Therefore, it is necessary to evaluate the airspace utilization of the terminal area in hazardous weather. This paper analyzed the impact of blocky hazardous weather area on flight path, flight distance and flight time, and established a calculation model for the terminal area arrival airspace utilization rate in the blocky hazardous weather area based on the flow volume ratio based on the time and space constraints of the terminal area airspace operation. We selected a control terminal which was affected by the blocky hazardous weather area, then used the model to calculate the approach airspace utilization rate and compared it with the approach airspace utilization rate under normal weather conditions. The results show that the evaluation results of the established model can reflect the actual operating conditions.

Patients With Combined Thermal and Intraabdominal Injuries: More Salvageable Than Not.

This study aims to better characterize the course and outcome of the uncommon subset of trauma patients with combined thermal and intraabdominal organ injuries. The National Trauma Data Bank was queried for burn patients with intraabdominal injury treated in all U.S. trauma centers from July 1, 2011 to June 30, 2015. General demographics, Glasgow coma scale (GCS), shock index (SI), Abbreviated Injury Scale (AIS) for burn, Injury Severity Score (ISS), blood transfusions, and abdominal surgery were evaluated. During the 5-year study period, there were 334 burn patients with intraabdominal injury, 39 (13.2%) of which received abdominal surgery. Burn patients who underwent operations had more severe injuries reflected by higher SI, AIS, ISS, blood transfusion, and worse outcomes including higher mortality, longer hospital and ICU length of stay, and more ventilator days compared to patients who did not undergo an operation. Nonsurvivors also exhibited more severe injuries, and a higher proportion received abdominal operation compared to survivors. Multivariate logistic regression analysis revealed that GCS on arrival, SI, AIS, ISS, blood transfusion, and abdominal operation to be independent risk factors for mortality. Propensity score matching to control covariables (mean age, systolic blood pressure on arrival, GCS on arrival, SI, ISS, time to operation, blood transfusion, and comorbidities) showed that of trauma patients who received abdominal operation, those with concomitant burn injury exhibited a higher rate of complications but no significant difference in mortality compared to those without burns, suggesting that patients with concomitant burns are not less salvageable than nonburned trauma patients.

Waypoint Position Optimization for Accurate Ground-Based Arrival Time Control of Aircraft

In the ground-based arrival time control operation, the measurement of the arrival time error and the speed advisory to cancel it are performed at specific waypoint(s). It is generally recognized that the arrival time accuracy depends on the accuracy of trajectory prediction and control and the availability of information about the aircraft intent. Additionally, the position of the waypoint also determines the arrival time accuracy. Therefore, in this study, the feasibility of waypoint position optimization to improve the arrival time accuracy of aircraft controlled by ground-based speed advisory and its effectiveness are discussed. The models of flight time uncertainty and the arrival time control window were developed using the actual track and numerical weather forecast data. An optimization problem was formulated that defined the arrival time uncertainty at the terminal point as the objective function and numerically solved using sequential quadratic programming. Through numerical investigations, the feasibility of the waypoint position optimization and its effectiveness were clearly demonstrated in comparison with uniform arrangement cases. It was also clarified that the advantage of the multiple waypoint application was enhanced when larger arrival time control windows were available.

A bi-objective model for robust berth allocation scheduling

Berth allocation is an important port operation problem for container terminals. This paper studies how to develop a robust schedule for berth allocation that incorporates a degree of anticipation of uncertainty (e.g., vessels’ arrival time and operation time) during the schedule’s execution. This study proposes a bi-objective optimization model for minimizing cost and maximizing robustness of schedules. A heuristic is also developed for solving the bi-objective model in large-scale problem cases. Numerical experiments are conducted to validate the effectiveness and efficiency of the proposed model and method. Managerial implications are also discussed.

A decision model for berth allocation under uncertainty

This paper studies the berth allocation problem (BAP) under uncertain arrival time or operation time of vessels. It does not only concern the proactive strategy to develop an initial schedule that incorporates a degree of anticipation of uncertainty during the schedule’s execution, but also studies the reactive recovery strategy which adjusts the initial schedule to handle realistic scenarios with minimum penalty cost of deviating from the initial schedule. A two-stage decision model is developed for the BAP under uncertainties. Moreover, a meta-heuristic approach is proposed for solving the above problem in large-scale realistic environments. Numerical experiments are conducted to validate the effectiveness and efficiency of the proposed method.

Arrival-Time Controllability of Tailored Arrival Subjected to Flight-Path Constraints

Recently, the continuous descent approach has been strongly focused because it can drastically reduce noise emission and fuel consumption during approach. The tailored arrival is proposed to facilitate the continuous descent approach at congested terminal airspace. In a tailored arrival operation, each arriving aircraft is provided with a specifically designed arrival path to fly on a continuous descent approach path without any conflict with other aircraft. In this paper, the arrival-time controllability of the tailored arrival paths determined by the top-of-descent and waypoint positions are discussed. A tailored arrival path is designed using the least number of track-to-fix and radius-to-fix legs neglecting the engine thrust. Two types of tailored arrival path are numerically investigated; one is determined by adjusting the waypoint positions with the fixed top of descent, and the other is determined by top-of-descent position with the fixed waypoints. They are numerically analyzed so as to satisfy a set of appropriate boundary conditions both at the top of descent and landing, and the behavior of the arrival-time difference from the standard continuous descent approach path is explored using a representative set of inputs. The arrival-time controllability is defined as the differences between the fastest and the slowest arrival time. Through several series of arrival-time analyses, it is found that the tailored arrival paths determined by changing the waypoint positions can achieve the larger arrival-time controllability compared with those determined by changing the top-of-descent position. It is also suggested that it is possible to compose an arrival path with the maximum arrival-time controllability without any additional fuel consumption.