Demand-capacity Imbalances Research Articles

Distinct phenotyping of critical patients with demand-capacity imbalance initiating acute renal replacement therapy by consensus clustering

BackgroundCertain patient subpopulations requiring dialysis initiation show varied survival rates and chances of ending renal replacement therapy (RRT). Consensus clustering can help identify these subgroups and their dialysis outcomes. MethodsThe study included patients who were over 18 years old with urine output above 400 ml per day and an estimated glomerular filtration rate over 15 ml/min/1.73 m2. They underwent acute RRT because of systemic demand-capacity imbalance. Using consensus clustering with 33 clinical variables and urea:creatinine ratio (UCR) to the variables to investigate the catabolic demand. Endpoints were all-cause mortality and being dialysis-free at 180-day follow-up after RRT initiation. ResultsOf 946 patients (mean 63 ± 17 years and 649 men, 68.6 %) three distinct phenotypes were identified. 509 (53.8%) patients died and 364 (38.5%) patients were weaned off dialysis. Cluster 2 showed better survival (60.23% vs. 53.18% [cluster 1] vs. 45.85% [cluster 3], P < 0.01) and higher possibility to be weaned off RRT (45.24% vs. 38.44% [cluster 1] vs. 31.62% [cluster 3], P < 0.01). High UCR had increased mortality (59.16% vs. 47.75%, P < 0.01) and a lower weaning rates (33.27%; 45.72%, P < .01). UCR with the clustering phenotype improved risk stratification. ConclusionsAmong critical patients undergoing RRT due to systemic demand-capacity imbalance, more than half of the patients died. We identified distinct phenotypes in demand-capacity imbalance in a heterogeneous cohort of patients initializing RRT. Additionally, we found that pre-dialysis UCR as a novel predictor for mortality and the likelihood of being dialysis-free.

Prediction of ATFM impact for individual flights: A machine learning approach

Air Traffic Flow Management regulations consist of issuing pre-departure ground delays to deal with demand-capacity imbalances in the airspace, smoothing out the demand at congested infrastructures. This results in discrepancies between the initially planned and executed (regulated) flights, negatively impacting operational efficiency and economic performance. To mitigate downstream effects, airspace users need to assess the severity and impact of these regulations when defining their operational plan. It is crucial to anticipate these potential network issues and plan for mitigation measures during the pre-tactical phase (the day before operations (D−1)). This article presents four independent machine learning models to estimate the impact of Air Traffic Flow Management regulations for individual flights during this pre-tactical phase. An integrated view is proposed to combine the results of the different models, displaying the appropriate level of information. Two models are designed to provide information about the probability of a given flight being affected by Air Traffic Flow Management regulations and the reason for this regulation (airport or airspace congestion). These models have reported accuracy levels between 82% and 87%. The remaining models estimate the impact of the delay (amount of Air Traffic Flow Management delay issued to the flight if regulated), with a Mean Absolute Error of 9.5 min when predicting this amount of delay. The SHapely Additive exPlanations analysis is used to identify the most important factors for detecting Air Traffic Flow Management regulations for individual flights during the pre-tactical phase from a data-driven perspective. An integrated view is proposed to create a system which combines the results of the four different models, displaying the appropriate level of information and avoiding an overload of information. Finally, the results are compared against models considering only static or post-operational data.

Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences

In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users’ trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible four-dimensional trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from the Eurocontrol data repository. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions to the problem facilitates a consensus between the network manager and airspace users. In view of the level of accuracy of the solutions and the excellent computational performance, we are optimistic that the proposed approach can make a significant contribution to the development of the next generation of air traffic flow management tools.

Dynamic Ventilatory Reserve During Incremental Exercise: Reference Values and Clinical Validation in Chronic Obstructive Pulmonary Disease.

Rationale: Ventilatory demand-capacity imbalance, as inferred based on a low ventilatory reserve, is currently assessed only at peak cardiopulmonary exercise testing (CPET). Peak ventilatory reserve, however, is poorly sensitive to the submaximal, dynamic mechanical ventilatory abnormalities that are key to dyspnea genesis and exercise intolerance. Objectives: After establishing sex- and age-corrected norms for dynamic ventilatory reserve at progressively higher work rates, we compared peak and dynamic ventilatory reserve for their ability to expose increased exertional dyspnea and poor exercise tolerance in mild to very severe chronic obstructive pulmonary disease (COPD). Methods: We analyzed resting functional and incremental CPET data from 275 controls (130 men, aged 19-85 yr) and 359 Global Initiative for Chronic Obstructive Lung Disease patients with stage 1-4 obstruction (203 men) who were prospectively recruited for previous ethically approved studies in three research centers. In addition to peak and dynamic ventilatory reserve (1 - [ventilation / estimated maximal voluntary ventilation] × 100), operating lung volumes and dyspnea scores (0-10 on the Borg scale) were obtained. Results: Dynamic ventilatory reserve was asymmetrically distributed in controls; thus, we calculated its centile distribution at every 20 W. The lower limit of normal (lower than the fifth centile) was consistently lower in women and older subjects. Peak and dynamic ventilatory reserve disagreed significantly in indicating an abnormally low test result in patients: whereas approximately 50% of those with a normal peak ventilatory reserve showed a reduced dynamic ventilatory reserve, the opposite was found in approximately 15% (P < 0.001). Irrespective of peak ventilatory reserve and COPD severity, patients who had a dynamic ventilatory reserve below the lower limit of normal at an isowork rate of 40 W had greater ventilatory requirements, prompting earlier attainment of critically low inspiratory reserve. Consequently, they reported higher dyspnea scores, showing poorer exercise tolerance compared with those with preserved dynamic ventilatory reserve. Conversely, patients with preserved dynamic ventilatory reserve but reduced peak ventilatory reserve reported the lowest dyspnea scores, showing the best exercise tolerance. Conclusions: Reduced submaximal dynamic ventilatory reserve, even in the setting of preserved peak ventilatory reserve, is a powerful predictor of exertional dyspnea and exercise intolerance in COPD. This new parameter of ventilatory demand-capacity mismatch may enhance the yield of clinical CPET in the investigation of activity-related breathlessness in individual patients with COPD and other prevalent cardiopulmonary diseases.

Systemic Determinants of Exercise Intolerance in Patients With Fibrotic Interstitial Lung Disease and Severely Impaired DLCO.

The precise mechanisms driving poor exercise tolerance in patients with fibrotic interstitial lung diseases (fibrotic ILDs) showing a severe impairment in single-breath lung diffusing capacity for carbon monoxide (DLCO < 40% predicted) are not fully understood. Rather than only reflecting impaired O2 transfer, a severely impaired DLCO may signal deranged integrative physiologic adjustments to exercise that jointly increase the burden of exertional symptoms in fibrotic ILD. Sixty-seven subjects (46 with idiopathic pulmonary fibrosis, 24 showing DLCO < 40%) and 22 controls underwent pulmonary function tests and an incremental cardiopulmonary exercise test with serial measurements of operating lung volumes and 0-10 Borg dyspnea and leg discomfort scores. Subjects from the DLCO < 40% group showed lower spirometric values, more severe restriction, and lower alveolar volume and transfer coefficient compared to controls and participants with less impaired DLCO (P < .05). Peak work rate was ∼45% (vs controls) and ∼20% (vs DLCO > 40%) lower in the former group, being associated with lower (and flatter) O2 pulse, an earlier lactate (anaerobic) threshold, heightened submaximal ventilation, and lower SpO2 . Moreover, critically high inspiratory constrains were reached at lower exercise intensities in the DLCO < 40% group (P < .05). In association with the greatest leg discomfort scores, they reported the highest dyspnea scores at a given work rate. Between-group differences lessened or disappeared when dyspnea intensity was related to indexes of increased demand-capacity imbalance, that is, decreasing submaximal, dynamic ventilatory reserve, and inspiratory reserve volume/total lung capacity (P > .05). A severely reduced DLCO in fibrotic ILD signals multiple interconnected derangements (cardiovascular impairment, an early shift to anaerobic metabolism, excess ventilation, inspiratory constraints, and hypoxemia) that ultimately lead to limiting respiratory (dyspnea) and peripheral (leg discomfort) symptoms. DLCO < 40%, therefore, might help in clinical decision-making to indicate the patient with fibrotic ILD who might derive particular benefit from pharmacologic and non-pharmacologic interventions aimed at lessening these systemic abnormalities.

#4980 THE ROLE OF EARLY ACUTE KIDNEY REPLACEMENT THERAPY (AKRT) IN THE PROGNOSIS OF PATIENTS WITH COVID-19: A RANDOMIZED CLINICAL TRIAL

Abstract Background and Aims The COVID-19 pandemic has increased the burden of morbidity and mortality worldwide and in its moderate to severe forms is associated with serious complications such as Acute Kidney Injury (AKI). AKI related to COVID-19 appears to be multifactorial and the AKI incidence is around 20%, with the need for dialysis in up to 40-50% of critically ill patients. So far, there has been no evidence of benefit from early dialysis in different contexts of AKI. In the context of COVID-19 it is possible that the indication of early dialysis support can help and improve outcomes with inflammation and volume control. The objective of the study was to evaluate the role of early acute kidney replacement therapy (AKRT) in the context of COVID-19. Method We analyzed 108 patients from a randomized open-label clinical trial with AKI 3 according to the KDIGO 2012 criteria, with severe acute respiratory syndrome with confirmed COVID-19 (RT- PCR technique or serology), admitted from March 2020 to May 2021 in a reference public hospital in the State of São Paulo (Brazil). Patients were randomized to early AKRT indication (either cytokine storm or fluid balance cumulative greater than 3% of body weight) or standard indication (classic indications or systemic demand and renal capacity imbalance). Cytokine storm was characterized as uninterrupted fever (38ºC or more) for at least 12 hours and exclusion criteria were patients younger than 18 years, pregnant women, chronic kidney disease (CKD) stages 4 and 5, kidney transplant recipients. Patients were randomized in blocks for allocation to groups and the primary outcome was hospital mortality. The study was approved by the research ethics committee, registered in the Brazilian Clinical Trials Registry. Seeking a detection of a 20% mortality difference between the standard and early groups, the required sample size would be 48 patients per group and was assumed an alpha error of 5%. All analyzes were performed using SPSS 28.0.1.1 (15). Results 108 patients were included in the intention-to-treat analyses (41 in the early group and 67 in the standard group). 66,7% were men, 86,9% white, mean of age was 62,56 ± 14,25 years, 93,9% in intensive care unit, 100% in mechanical ventilation (MV) and 93,5% in use of vasoactive drugs. The most common comorbidities were hypertension (68,5%), diabetes (39,8%) and obesity (52,8%). Hematuria (47,9%) and proteinuria (74,7%) were common. The P/F ratio, ATN-ISS e APACHE II scores mean were, respectively, 145,40 ± 67,70; 0,75 ± 0,12 e 20,04 ± 6,64. 51,9% of the sample had a cytokine storm and the overall mortality was 87%. The standard and early groups were similar in gender, race, comorbidities, severity, medications, and laboratory markers of severity as well as catheter site, system coagulation, number of dialysis sessions and mortality (63,8% in the standard vs 36,2% in the early, p = 0,32). There was a difference in age e modality of dialysis between the groups with lower mean of age (57,78 ± 14,24 years vs 65,49±13,54) and more continuous dialysis (66,7% vs 33,3%, p&amp;lt;0,01) in the early group. Hypertension (68,9% vs 31,1% p = 0,051), CKD (80% vs 20% p = 0,038) and dyslipidemia (80,8% vs 19,2%, p = 0,52) were more common in the standard. The early group had a longer ICU stay (20,26±20,93 days vs 13,22±11,34, p = 0,053), lower maximum creatinine (3,88±1,88 vs 5,24±2,46, p = 0,004) and lower ATN-ISS (0,72±0,12 vs 0,78±0,12, p = 0,017). In the survival analysis, a better hospital survival trend was observed in the early group, but with no statistically significant difference (p = 0,061). Use of angiotensin converting enzyme inhibitor (ACEI), 42,9% vs 13,8, p = 0,008, and longer time in MV (28,71±12,48 days vs 13,42±13,99, p&amp;lt;0,001) were associated with lower mortality. In the logistic regression, use of ACEI (OR 0,007, 95% CI 0-0,29, p = 0,009), MV time (OR 0,93, 95% CI 0,88-0,99, p = 0,023) and higher APACHE II (OR 1,28, 95%CI 1,02-1,6, p = 0,03) were associated with mortality. Conclusion It was observed that the use of ACEI, longer ventilation time and APACHE II score were associated with mortality, while early AKRT indication had no impact in survival of patients.

Image-Based Multi-Agent Reinforcement Learning for Demand–Capacity Balancing

Air traffic flow management (ATFM) is of crucial importance to the European Air Traffic Control System due to two factors: first, the impact of ATFM, including safety implications on ATC operations; second, the possible consequences of ATFM measures on both airports and airlines operations. Thus, the central flow management unit continually seeks to improve traffic flow management to reduce delays and congestion. In this work, we investigated the use of reinforcement learning (RL) methods to compute policies to solve demand–capacity imbalances (a.k.a. congestion) during the pre-tactical phase. To address cases where the expected demands exceed the airspace sector capacity, we considered agents representing flights who have to decide on ground delays jointly. To overcome scalability issues, we propose using raw pixel images as input, which can represent an arbitrary number of agents without changing the system’s architecture. This article compares deep Q-learning and deep deterministic policy gradient algorithms with different configurations. Experimental results, using real-world data for training and validation, confirm the effectiveness of our approach to resolving demand–capacity balancing problems, showing the robustness of the RL approach presented in this article.

Impact of Aircraft Performance and Time of the Day on Flight Arrival Delays Prediction in the United States: a Machine Learning Classification

The excessive growth of air traffic, with the limited airspace and airports capacity, results in a flight demand-capacity imbalance leading to air traffic delays. This paper explores the factors associated with delay in both microscopic and macroscopic ways. The aim is to develop a model which analyzes and predicts the occurrence of flight arrival delays using US domestic flight data for the year 2018. It will provide passengers, airlines and airport managers with reliable flight arrival schedules, and consequently reduce economic losses and enhance passengers trust. Beside database features, the proposed model is to the best of our knowledge the first attempt to predict flight arrival delays using three new features which are contributive factors to delays: Departure Time and Arrival Time of the day in which the flight was performed (Early morning, late morning, noon, afternoon, evening or night) and model of aircraft. Four Machine Learning classifiers namely Random Forest, Decision Trees, K-Nearest Neighbors and Naive Bayes were used. In order to find the best parameters of each algorithm, we implemented Grid Search technique. The performance of each classifier was compared in terms of hyperparameters tuning, classification metrics and features description. The experimental results showed that the proposed system was able to predict flight arrival delays with the best Random Forest accuracy of 0.9356 and a higher number of correctly classified flights. To prove the importance of our findings, we compared our model to that of existing literature studies.

Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning

An environmentally and economically sustainable air traffic management system must rely on fast models to assess and compare various alternatives and decisions at the different flight planning levels. Due to the numerous interactions between flights, mathematical models to manage the traffic can be computationally time-consuming when considering a large number of flights to be optimised at the same time. Focusing on demand–capacity imbalances, this paper proposes an approach that permits to quickly obtain an approximate but acceptable solution of this problem. The approach consists in partitioning flights into subgroups that influence each other only weakly, solving the problem independently in each subgroup, and then aggregating the solutions. The core of the approach is a method to build a network representing the interactions among flights, and several options for the definition of an interaction are tested. The network is then partitioned with existing community detection algorithms. The results show that applying a strategic flight planning optimisation algorithm on each subgroup independently reduces significantly the computational time with respect to its application on the entire European air traffic network, at the cost of few and small violations of sector capacity constraints, much smaller than those actually observed on the day of operations.

Strategic Timing and Dynamic Pricing for Online Resource Allocation

This paper optimizes dynamic pricing and real-time resource allocation policies for a platform facing nontransferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogenous price and time sensitivities. We characterize the optimal mechanism, which specifies a dynamic menu of prices and allocations. Service timing and pricing are used strategically to: (i) dynamically manage demand-capacity imbalances, and (ii) provide discriminated service levels. The balance between these two objectives depends on customer heterogeneity and customers’ time sensitivities. The optimal policy may feature strategic idlenexss (deliberately rejecting incoming requests for discrimination), late service prioritization (clearing the queue of delayed customers), and deliberate late-service rejection (focusing on incoming demand by rationing capacity for delayed customers). Surprisingly, the price charged to time-sensitive customers is not increasing with demand—high demand may trigger lower prices. By dynamically adjusting a menu of prices and service levels, the optimal mechanism increases profits significantly, as compared with dynamic pricing and static screening benchmarks. We also suggest a less information-intensive mechanism that is history-independent but fine-tunes the menu with incoming demand; this easier-to-implement mechanism yields close-to-optimal outcomes. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process

The concept of User Driven Prioritisation Process (UDPP) was introduced to give Airspace Users (AUs) more flexibility under demand-capacity imbalance. This paper presents two UDPP-DCB models, built on the UDPP principle, using the Selective Flight Protection (SFP) approach to minimize the total delay cost. AUs are enabled to adapt their operations in a more cost-efficient way in the presence of capacity constraints in airspace, optimizing their flights to keep the priorities track. Then, ATFM integrates the AUs’ decision to reassign the time slots and the preferred rerouting trajectories. Results suggest that the delay cost for AUs can be largely reduced through applying the UDPP-DCB models proposed in this paper, while allowing rerouting proves effective in reducing the system delay cost.

Impact of Tailwind on Airport Capacity and Delay at Zurich Airport

Wind has a significant impact on the safety of airport operations. Crosswind affects flight stability, while head/tailwind affects the required runway length for landings and take-offs. Those two reasons make wind speed and direction an important factor in the runway configuration selection process. Strong crosswind calls for a runway orientation change, while the tailwind is usually associated with the switch of the runway direction in use. This paper analyses a specific case - the impact of tailwind on the runway configuration selection and, consequently, on airport capacity and delays generated due to demand-capacity imbalance. The problem of traffic delays caused by activation of unfavourable runway configurations due to tailwind is analysed using the case of Zurich Airport and traffic data collected over a five-month period in the summer of 2018. The main objective of the analysis was to identify possible areas of improvement in Air Traffic Control decision-making related to runway configuration activation due to tailwind.

Identification and Prediction of Disruptions in Airline Networks

Disruptions in the air transportation system oftentimes lead to demand-capacity imbalances, resulting in flight delays and cancellations as byproducts of traffic management and system recovery actions. In order to better predict the impact of disruptions, as well as provide more targeted and proactive system recovery actions, it is critical to unambiguously identify key characteristics such as: (1) When did the disruption occur, (2) what was the duration of the disruption and subsequent recovery, and (3) how will an ongoing disruption evolve. In particular, providing performance measures regarding the duration, intensity, and type of disruption is straightforward for individual airports; for a large, geographically disparate, and interconnected network of airports, these characteristics are less clear. To address this, we first formalize the notion of \textit{disruption-recovery trajectories} (DRTs) for the air transportation system through representing key network delay and cancellation performance measures as evolving between discrete states. These DRTs capture information regarding both the magnitude and spatial impact of air transportation system disruptions. Using this DRT framework, we report on past disruption and recovery characteristics for four major US airlines, as well as demonstrate the ability to predict short-term evolution of disruptions and recoveries without detailed schedule information, potentially enabling near-real time decision support for prescriptive management actions.

Analysis of airport weather impact on on-time performance of arrival flights for the Brazilian domestic air transportation system

Air traffic operations are significantly impacted by weather conditions. These external factors may impose operational constraints and generate demand-capacity imbalances, leading to reduced on-time performance, additional airline costs and inconveniences to passengers. Efficient management of such disruptions requires an understanding of the main causes of flight delays towards increasing their predictability. In this study, we investigate the impacts of airport surface weather conditions on the likelihood of flight delays for the Brazilian domestic air transportation system. We use historical flight schedule, on-time performance and weather data and estimate a logit model to analyze how different meteorological variables at the airport of destination affect the probability of a delayed arrival. We obtain empirical evidence for the impacts of low ceiling and visibility conditions, precipitation and wind gusts on the likelihood of arrival delays for the set of Brazilian airports analyzed.

Airport Connectivity Optimization for 5G Ultra-Dense Networks

The rapid increase of air traffic demand and complexity of radio access network motivate developing scalable wireless communications by adopting system intelligence. The lack of adaptive reconfiguration in radio transmission systems may cause dramatic impacts on the traffic management concerning congestion and demand-capacity imbalances. This has driven the industry to jointly access licensed and unlicensed bands for improved airport connectivity. Therefore, intelligent system is embedded into fifth generation (5G) ultra-dense networks (UDNs) to provision dense and irregular deployments that support extended coverage and improved energy-efficiency for the entire airport network at higher throughputs. To define the technical aspects of the proposed solution, this paper addresses new intelligent technique that configures the coverage and capacity factors of radio access network considering the changes in air traffic demands. This technique is analysed through mathematical models that employ power consumption constraints to support dynamic traffic control requirements to improve the overall network capacity. The presented problem is formulated for medium or large airport air transportation network. The power optimization problem is solved using linear programming with careful consideration to latency and energy efficiency factors. Specifically, an intelligent pilot power method is adopted to maintain the connectivity throughout multi-interface technologies by assuming minimum power requirements. Numerical and system-level analysis are conducted to validate the performance of the proposed schemes for both licenced macrocell New Radio (NR) and unlicensed wireless fidelity (WiFi) topologies. Finally, the insights of problem modelling with intelligent techniques provide significant advantages at reasonable complexity and brings the great opportunity to improve the airport network capacity.

A Simultaneous Optimization Model for Airport Network Slot Allocation under Uncertain Capacity

Serious congestion and delay problems exist in most of the busiest airports worldwide because of imbalance between scarce airport slot resources and increasing traffic demand. Various factors, especially weather conditions, exacerbate the demand–capacity imbalance. This paper presents a robust model for simultaneous slot allocation on an airport network in multiple calendar days, considering airport capacity uncertainty. The idea of robust optimization is conducive to sustainable and stable decision-making. Robustness is represented through reducing the potential scheduling conflicts in the worst case. Then the model links the strategic decisions and pre-tactical decisions in air traffic management (ATM) through the tradeoff between strategic discrepancy cost and operational congestion cost. Under the support of the Cplex solver, numerical analyses are taken to validate the characteristics and effectiveness of the proposed model. The results show that the proposed model effectively eliminates the existing and potential scheduling conflicts, and makes effective tradeoffs between airline preference and potential airport congestion risk.

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine (SVM) model to explore the non-linear relationship between flight delay outcomes. Individual flight data were gathered from 20 days in 2018 to investigate causes and patterns of air traffic delay at three major New York City airports. Considering the black box characteristic of the SVM, a sensitivity analysis was performed to assess the relationship between dependent and explanatory variables. The impacts of various explanatory variables are examined in relation to delay, weather information, airport ground operation, demand-capacity, and flow management characteristics. The variable impact analysis reveals that factors such as pushback delay, taxi-out delay, ground delay program, and demand-capacity imbalance with the probabilities of 0.506, 0.478, 0.339, and 0.338, respectively, are significantly associated with flight departure delay. These findings provide insight for better understanding of the causes of departure delays and the impacts of various explanatory factors on flight delay patterns.

Integer Programming Model for Solving Delay Problem in Air Traffic Management System

Nowadays, due to the rapid growth of air traffic and airspace congestion flight delays are becoming serious problems. This situation happens due to the demand capacity imbalances in air traffic management system. The deterioration of the expected service and operational costs would be the direct impact of this problem. Network-wide air traffic flow management can be regarded as an effective way to ease demand-capacity imbalances globally and would be able to reduce airspace congestion and flight delays. This paper proposes an integer programming model to solve the problem. The objective function consists of minimizing operational costs and risk costs.

Impact Analysis of Demand Management on Runway Configuration in Metroplex Airports

Congestion in airports and metroplexes is increasingly becoming a key bottleneck in the global air transport system. It is largely due to inefficient utilization of runway resources and its consequences of imbalance between demand and capacity. Existing studies mainly focus on runway configuration in a single airport system, and little consideration is given to the impact of demand management on runway configuration in metroplexes. Therefore, this paper proposes a methodology and assessment framework for runway configuration with a focus on the exploitation of multiple active runways in metroplex airports. The primary innovation is an integrated runway configuration management formulation with air traffic demand management options. The first is static and dynamic runway configuration management models featuring three optimization objectives, four cases of demand-capacity imbalance, three air traffic demand management options and three assessment scenarios, for eleven priority settings. The second is an efficient multi-objective evolutionary algorithm with a mechanism of objective-guided individual selection, which can obtain close-to optimal solutions with a very low computational cost within 6 seconds. Computational experiments for the real-world case of the Shanghai metroplex airports show that, the inducing strategy of minimizing the total number of adjusted flights is the best mechanism for making satisfactory tradeoffs among multiple objectives in dynamic runway configuration. The proposed model reduces the total number of adjusted flights by an average of 36% compared with the baseline static runway configuration management. Furthermore, unlike the conventional approach of giving priority to arriving aircraft, a higher priority for departures is more effective in enhancing the performance of runway systems and reducing the number of adjusted flights. The proposed framework can be applied at pre-tactical (e.g., one-day planning) as well as tactical (e.g., several-hours rolling horizon) levels.

Generalized Method of Modeling Minute-in-Trail Strategy for Air Traffic Flow Management

With the rapidly increasing air traffic demand, the demand-capacity imbalance problem of sector is surfaced gradually. And, minute-in-trail/miles-in-trail (MIT) is an effective strategy to balance the traffic demands and capacity. In this work, we consider the MIT strategy generation problem for the situation that a sector with NC corridors is affected by convection weather for Timb time periods. Given the sector capacity Cwt, t=1,…,Timb, under convection weather, we propose a three-phase optimization framework to generate E-MIT strategy to achieve the demand-capacity balance. First, we take the sector capacity of Timb time periods under convection weather as a whole, that is, ∑t=1TimbCwt, and then a dynamical programming-based method is proposed to allocate ∑t=1TimbCwt for NC corridors such that the capacity resources Awi of each corridor CORi, i=1,…,NC, can be determined. Second, a 0-1 combination algorithm is used to allocate the capacity resources Awi into Timb time periods for each corridor CORi such that the candidate strategies set CSi of each corridor can be determined, where a strategy solji∈CSi is an array with Timb numbers and each number represents the maximum allowed number of flights entering into sector from CORi in one time period. Finally, a modified shortest path algorithm based on the backtracking method is taken to select the optimal strategy from CSi for NC corridors such that the total delay cost and air traffic control load are minimized. Additionally, a dynamical programming-based method is proposed to generate E-MIT strategy for the special case that the sector capacities of different time periods under convection weather are the same, that is, Cw1=Cw2=⋯=CwTimb, and the generated strategies of Timb time periods for a corridor are also the same. Experimental results show that compared with the proposed three-phase optimization method, rate-based method and need-based method will spend more 8.1% and 6.3% of delay cost, respectively. When considering the special case, the experimental results show that compared with the proposed dynamical programming-based method, the rate-based method and need-based method will spend more 10.2% and 7.5% of delay cost, respectively.