基于多种场面冲突形式的离场延误预测

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.

In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.

The usage of big data analytics is booming today, with its ability to be used to draw useful insights from past data research. Its uses in the aviation industry have a wide array of applications ranging from predicting flight delays to detecting faults in airplane parts. In this paper, we conducted exploratory data analysis on flight dataset to draw inferences on arrival and departure delays and to identify relationships between flight timings and delays. Using the flight delay data, we identified which flight is mostly prone to delays. The arrived upon conclusions are useful for selecting flights in the future.

Flight delay is the root of Delay Propagation, while delay propagated within a Flight Chain. Busy hub-airport plays an important role in a Flight Chain, since the Initial Delay often happens there. To research more possible impacting factors in the Flight Chain, a busy hub-airport is chosen as the object in this research. After analyzing the complex and uncertain relationship between flight delays and all possible impacting factors, Bayesian network (BN) adapt to model and estimate about flight delay in a busy hub-airport. We proposed two learning methods with three different models. Model I is an estimation model for Arrival Delay based on parameter learning of BN with the Expectation-Maximization (EM) algorithm. Model II is estimation model for Arrival Delay based on structure learning of BN with K2 algorithm. The model learned by K2 is proved more suitable for modeling the estimation of flight delay, with a higher estimating correct rate than Model I. Then Model II is estimation model for Departure Delay with the same algorithm of Model II.

Flight delays are a significant problem for society as they evenly impair airlines, transport companies, air traffic controllers, facility managers, and passengers. Studying prior flight data is an essential activity for every player involved in the air transportation system. Besides, developing accurate prediction models for flight delays is a crucial component of the decision-making process. Prescribing actions to solve on-going delays is an even challenging task due to the air transportation system complexity. In this regard, this paper presents a thorough literature review of data science techniques used for investigating flight delays. This work proposes a taxonomy and compiles the initiatives used to address the flight delay studies. It also offers a systematic literature review that describes the trends of the field and methods to analyse the applicability of newly proposed methods.

Interferometric phases have fluctuations that result from decorrelation between the complex images in an interferometric pair. Recently it has been proposed that a PDF derived for multilook polarimetric copolar-phase data can also be applied to interferometric SAR data. This PDF is parameterized by the number of looks and the complex correlation coefficient. Such a PDF allows the authors to determine phase error as a function of decorrelation. Actual SAR data usually have an affective number of looks less than the nominal number of looks. As a result, the assumption that samples are independent does not hold. Rederivation of the PDF to account for the nonindependence of samples represents a challenging, if not intractable, problem. Using /spl chi//sup 2/-squared goodness-of-fit tests, however, the authors show that an effective number of looks can be chosen in place of the nominal number of looks. The authors initially test this model using simulated data that has the same spectral characteristics as ERS-1 data. In addition to the phase fluctuations caused by decorrelation, actual data also have fluctuations caused by the topography and the imaging geometry. As a result, it is difficult to compare the phase statistics predicted by the PDF with actual data. There are a few cases, however, where these additional fluctuations are negligible and statistical comparisons can be made. They find that with an effective number of looks less than the nominal number, this PDF provides a good statistical model for multilook interferometric phases from both simulated and actual ERS-1 data. >

This paper discusses the results of modeling and simulation performed by FAA’s Performance Analysis and Strategy office to forecast delays at thirty major US airports six months ahead of time. In order to have reliable delay forecasts, it is important that the simulation model validates well with historic actual data. As part of the validation efforts, we recognize that an aircraft’s movement in the US National Airspace System (NAS) involves its interaction with various resources along the flight path, such as airport runways, arrival and departure fixes, airspace sectors, and terminal gates. When multiple aircraft compete for access to these resources, their movements are impeded due to operational and safety considerations, resulting in flight delays. The objective of this study is to understand how various resources contribute to total flight delays, and how sensitive the delays are to changes in resource capacities. The results to date indicate that reduced airport runway capacities play a significant role in overall delays, but arrival and departure fixes and airspace sector constraints do not. Our use of historical airport AARs and ADRs for runway capacities, which may inherently account for fix constraints, yields delays that agree very well with actual values reported in ASPM. However, care has to be taken to use and interpret these rates when there are runway additions or closures. One area in which all NAS-wide models fail to replicate the behavior of the NAS is gate-out delay. Our simulation includes modeled distributions to capture gate-out delays, but they were found to capture only a fraction of actual gate-out delays. Terminal gate constraints were added to verify if they yielded large gate-in delays that would result in additional gate-out delays. No significant additional gate-out delays were obtained, although we gained better insight into how gate-in delays may occur. We are continuing to research potential NAS interactions that would account for the gap in predicting gate-out delays. Additional simulation model enhancements are planned to account for traffic flow initiatives such as GDP, as well as itinerary adjustments due to flight cancellation and substitutions.

Airplane transportation has many advantages compared to other means of transportation, where this means of transportation is able to cover long distances in a short time. Besides having advantages, of course, air transportation often experiences flight delays which can be caused by several things, including delayed flights due to late arrival (Arrival Delay), delays of the airline itself (Carrier Delay), previously late arrivals (Late Aircraft Delay), air traffic congestion (Nas Delay), security issues (Security Delay) and weather conditions (Weather Delay) The tests carried out were 101,315 US flight delay data from 2017 to 2022. By using the Machine Learning method, the results obtained were that the largest flight delays were caused by late arrivals, namely 386,124,672, where the largest flight delays were by airlines. The airline, namely Southwest Airlines, is 61,474,379 of the total airlines, which is 20, and the biggest departure delay is at Chicago O'Hare International airport, which is in the city of Chicago, IL province, which is 20,912,928 of the total airport, which is 596. This research aims to predict future flights by analyzing current flight data, so that future flights can be better by overcoming or avoiding previous flight delay problems.

The rapid growth of air traffic demand and limited airspace resources have made efficient coordination in multi-airport systems a critical challenge. This paper develops a bi-level air–ground collaborative scheduling model for the Beijing-Tianjin-Airport cluster, integrating terminal-area departure sequencing (upper level) with airport surface taxi and pushback scheduling (lower level), where the upper-level model minimizes departure delays, maximizes airport satisfaction, and reduces fairness deviation, while the lower-level model optimizes taxi routing and pushback timing. To solve the model, NSGA-II is applied to the upper-level sequencing problem and a Genetic-Simulated Annealing algorithm is used for surface scheduling. Empirical evaluation using operational data from Beijing Capital, Beijing Daxing, and Tianjin Binhai airports shows that the proposed approach reduces total departure delay by 49.4%, lowers average taxi time by up to 40.4%, and improves overall airport satisfaction by 5.2%, while reducing fairness deviation by 52.6%. These results demonstrate that the framework effectively enhances efficiency and equity in multi-airport departure operations.

In order to optimize the recovery scheduling of large-scale flight delays, the loss constitution of flight delays is analyzed after considering the economic benefits and social impact of flight delays, and a new recovery scheduling model of large-scale flight delays is put forward. A hybrid particle swarm optimization (HPSO) is designed by introducing local search method into particle swarm optimization. Simulations are carried out by using flight data from a domestic airport. The results show that HPSO has obvious advantages over other scheduling algorithms, and the purposes of optimizing the sequencing of the flights and reducing the economic losses are achieved.

Flight delays are unavoidable, and they have a substantial financial impact on airlines. Airlines demand an accurate flight delay estimate since the data may be used to improve customer satisfaction and profitability. Many research have been conducted on flight delay modeling and prediction, with the majority of them attempting to predict the delay by extracting important qualities and variables. Due to the large volume of data, dependencies, and large number of parameters, most of the offered solutions are insufficiently accurate. This research presents a Deep Learning-based approach for anticipating aircraft delays (DL).DL is a relatively recent method for tackling issues with a high level of complexity and a large number of data. Besides, DL can extricate key highlights from information naturally. In expansion, since most flight delay information is boisterous, a technique based on stack denoising autoencoder is created and included within the proposed model. Furthermore, the Levenberg-Marquart calculation is utilized to decide weight and inclination fitting values, and the yield is at that point tuned to convey exceptionally precise comes about. Two more structures are created to investigate the effect of the stack denoising autoencoder and the LM method on the model structure.

Departure and arrival delay data in an 8-month period of Beijing Capital International Airport (BCIA) were analyzed to gain insights into probabilistic distributions of delays, in the hope that the delay characterizations in terms of stochastic distributions drawn from our work could improve demand predictions in air traffic flow management systems. Specifically we construct BCIA's delay characteristics using probability density functions based on an optimized Generalized Extreme Value (GEV) model. Analysis and verification through application of BCIA's history flight delay data demonstrate better Goodness of Fit.

The main goal of this article was examination of factors influencing flights delay at three most important international airports in Czech Republic. Data of selected Airlines operating in Czech Republic, whose flights are mainly oriented to international airports in Prague, Brno and Ostrava, were used for needs of this article. Analysis of contingency tables including Pearson chi-squared test was used for data processing. Dependences were presented in graphical form by correspondence analysis. Results from analysis showed that delay caused by technical reasons and maintenance is the most frequent in Prague as well as delay caused by high concentration of airspace, operational management and crew duty norms. Problems caused by departure delay from previous destination are significantly more frequent in Brno and Ostrava by reason of small number of alternative available aircraft. Delays caused by supplier (handling, catering, …) are mostly short, in particular by reason of potential penalty. Delays caused by technical problems and necessary maintenance service last mostly longer time and are more frequently on aircraft of type Boeing. Delays of borrowed aircraft of type Airbus are more frequently caused by rental and control of this aircraft by other companies which causes communication and planning difficulties.

An econometric model of average daily delay is formulated and estimated to analyze flight delay in the U.S. domestic system. The model considers the effects of arrival queuing, volume, terminal weather, en route weather, seasonal effects, and secular effects. In particular, the time-of-day effects of arrival queuing, the effects of scheduled arrivals, and the interaction between scheduled arrivals and weather conditions are investigated. The estimation results suggest that ( a) queuing has a greater delay impact in the morning than in the afternoon or evening (one unit of morning queuing delay causes about three times as much average daily delay as one unit of evening queuing delay), ( b) scheduled arrivals–-both alone and in interaction with weather conditions–-significantly affect average delay, and ( c) 31% of the total delay increase between early 2004 and early 2005 can be attributed to traffic growth.

The power generation characteristics of bifacial PV module on water surface are complicated. This paper proposed a water surface reflectivity model, which takes the light reflection characteristics on the calm water surface and the fluctuating water surface into consideration. The experimental data shows that the average absolute difference between actual water surface reflectivity and simulation data is 0.88%. A power generation model of the bifacial PV module was proposed on the basis of considering of the background reflectivity and the feature of bifacial PV module. The experimental data shows that the average absolute difference between actual PV power generation and simulation data is 2.1%. An approximate power generation enhancement ratio was proposed to estimate the power generation of bifacial PV module. Experimental results show that the average absolute difference between the approximate data and the actual data is 0.7%.