Journal articles on the topic 'Flight Delay'
To see the other types of publications on this topic, follow the link: Flight Delay.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Flight Delay.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Zheng, Zhe, Wenbin Wei, and Minghua Hu. "A Comparative Analysis of Delay Propagation on Departure and Arrival Flights for a Chinese Case Study." Aerospace 8, no. 8 (August 4, 2021): 212. http://dx.doi.org/10.3390/aerospace8080212.
Full textAbstract:
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.
2
Cao, Yakun, Chenping Zhu, Yanjun Wang, and Qingyun Li. "A Method of Reducing Flight Delay by Exploring Internal Mechanism of Flight Delays." Journal of Advanced Transportation 2019 (December 30, 2019): 1–8. http://dx.doi.org/10.1155/2019/7069380.
Full textAbstract:
This paper explores the internal mechanism of flight departure delay for the Delta Air Lines (IATA-Code: DL) from the viewpoint of statistical law. We roughly divide all of delay factors into two sorts: propagation factor (PF), and nonpropagation factors (NPF). From the statistical results, we find that the distribution of the flight departure delay caused by only NPF exhibits obvious power law (PL) feature, which can be explained by queuing model, while the original distribution of flight departure delay follows the shift power law (SPL). The mechanism of SPL distribution of flight departure delay is considered as the results of the aircraft queue for take-off due to the airports congestion and the propagation delay caused by late-arriving aircraft. Based on the above mechanism, we develop a specific measure for formulating flight planning from the perspective of mathematical statistics, which is easy to implement and reduces flight delays without increasing operational costs. We analyze the punctuality performance for 10 of the busiest and the highest delay ratio airports from 155 airports where DL took off and landed in the second half of 2017. Then, the scheduled turnaround time for all flights and the average scheduled turnaround time for all aircraft operated by DL has been counted. At last, the effectiveness and practicability of our method is verified by the flights operation data of the first half of 2018.
3
Jacquillat, Alexandre. "Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs." Transportation Science 56, no. 2 (March 2022): 265–98. http://dx.doi.org/10.1287/trsc.2021.1081.
Full textAbstract:
Ground delay programs (GDPs) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may not result in optimal outcomes for passengers—especially with connecting itineraries. This paper proposes a novel passenger-centric optimization approach to GDPs by balancing flight and passenger delays in large-scale networks. For tractability, we decompose the problem using a rolling procedure, enabling the model’s implementation in manageable runtimes. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs through two main mechanisms: (i) delay allocation (delaying versus prioritizing flights) and (ii) delay introduction (holding flights to avoid passenger misconnections). In practice, however, passenger itineraries are unknown to air traffic managers; accordingly, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly. Results show that the proposed passenger-centric approach is highly robust to imperfect knowledge of passenger itineraries and can provide significant benefits even in the current decentralized environment based on collaborative decision making.
4
Guo, Ziyu, Guangxu Mei, Shijun Liu, Li Pan, Lei Bian, Hongwu Tang, and Diansheng Wang. "SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction." Sensors 20, no. 22 (November 11, 2020): 6433. http://dx.doi.org/10.3390/s20226433.
Full textAbstract:
There has been a lot of research on flight delays. But it is more useful and difficult to estimate the departure delay time especially three hours before the scheduled time of departure, from which passengers can reasonably plan their travel time and the airline and airport staff can schedule flights more reasonably. In this paper, we develop a Spatio-temporal Graph Dual-Attention Neural Network (SGDAN) to learn the departure delay time for each flight with real-time conditions at three hours before the scheduled time of departure. Specifically, it first models the air traffic network as graph sequences, what is, using a heterogeneous graph to model a flight and its adjacent flights with the same departure or arrival airport in a special time interval, and using a sequence to model the flight and its previous flights that share the same aircraft. The main contributions of this paper are using heterogeneous graph-level attention to learn the influence between the flight and its adjacent flight together with sequence-level attention to learn the influence between the flight and its previous flight in the flight sequence. With aggregating features from the learned influence from both graph-level and sequence-level attention, SGDAN can generate node embedding to estimate the departure delay time. Experiments on a real-world large-scale data set show that SGDAN produces better results than state-of-the-art models in the accurate flight delay time estimation task.
5
Sherry, Lance. "A model for estimating airline passenger trip reliability metrics from system-wide flight simulations." Journal of Transport Literature 7, no. 2 (April 2013): 319–37. http://dx.doi.org/10.1590/s2238-10312013000200017.
Full textAbstract:
Analysis of the benefits of government modernization initiatives for airports or air traffic control are conducted using complex software models that simulate up to 60,000 flights per day. These flight-centric simulations do not model passenger flows and therefore do not account for passenger trip delays due to cancelled flights and missed connections, which account for up to 60% of the total passenger trip delays. This paper describes a closed-form model for estimating passenger trip reliability metrics from flight delay data from system-wide simulations. The outputs of the model, (i) percent passengers disrupted, (ii) average passenger trip delay, and (iii) total passenger trip delays, are derived from the probability of delayed flights and network structure parameters. The model highlights the role of network structure, in addition to flight on-time performance, on passenger trip reliability. These results have implications for government and industry initiatives to improve flight on-time performance through modernization, consumer protection, and the conduct of benefits analysis.
6
Zheng, Zhe, Wenbin Wei, Bo Zou, and Minghua Hu. "How Late Does Your Flight Depart? A Quantile Regression Approach for a Chinese Case Study." Sustainability 12, no. 24 (December 17, 2020): 10553. http://dx.doi.org/10.3390/su122410553.
Full textAbstract:
Flight departure delays cost airlines and airports millions of dollars and become a systematic problem. The on-time performance at an airport is connected to and easily affected by delay propagation from previous operations of flights using the airport. In this paper, we employ both Ordinary Least Square (OLS) and quantile regressions to investigate the impact of various influencing factors on flight departure delay. By using historical flight records and weather information, the impacts of delay propagation-related and other factors are quantified to study the correlations between the explanatory and response variables. Three variables, including previous arrival delay, turnaround buffer time, and the first order of a day, are used to examine the propagation effects. We find that aircraft type, flying on a weekday, and being the first flight of a day have significant impacts on short departure delays. Ground buffer is conducive to mitigating delay propagation. For long delays, however, ground buffer cannot work in an efficient way, and the previous arrival effect is more important. Convective weather and aircraft type are the crucial factors in this situation. Interestingly, flying on a weekday suddenly becomes one of the main components under extreme delays. Meanwhile, propagated delay and airport congestion remain significantly impactful on the on-time performance.
7
Atallah, Stephanie, and Susan Hotle. "Evaluation of Airport Size and Delay Causal Factor Effects on Delay Propagation Dissipation." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 3 (November 17, 2021): 608–20. http://dx.doi.org/10.1177/03611981211055663.
Full textAbstract:
The International Civil Aviation Organization identifies departure and arrival punctuality as on-time key performance indicators. However, these metrics assume a flight’s delay is a result of either the origin or destination airport, providing limited information on where delay should be mitigated in the U.S. National Airspace System (NAS). This study evaluates the relationship between delay propagation magnitude, delay causal factor, airport size, and charged facility (airport or Air Route Traffic Control Center), to examine if certain delays take longer to dissipate. First, using flights from July 2018, results show that most delay propagation chains originate at large-hub airports. However, these delays were the quickest to recover. Second, this study presents a regression model, predicting total propagated delay using fixed effects based on the weather region where the original delay occurred. Each additional flight affected by downstream delay adds 18.7 min on average to total arrival delay in a propagation chain. Additionally, if weather was the original causal factor, total propagated delay increased by 11.6 min compared with non-weather delays. Lastly, this study compares delay propagation in July 2018 and July 2020. Results show uneven impacts of the coronavirus disease 2019 (COVID-19) across the large-hub airports. Some of the investigated airports did not witness large improvements in average delay per delayed flight, warranting further research in the future. While delay and delay propagation have not been completely eradicated in the NAS during the COVID-19 pandemic, findings suggest that both have significantly declined on average.
8
Shao, Quan, Mengxue Shao, Yunpeng Bin, Pei Zhu, and Yan Zhou. "Flight Recovery Method of Regional Multiairport Based on Risk Control Model." Mathematical Problems in Engineering 2020 (April 29, 2020): 1–18. http://dx.doi.org/10.1155/2020/7105381.
Full textAbstract:
In the regional multiairport system, the contradiction between the limited operating resources and the large flight flow is serious, and the flight delays can easily lead to the occurrence of unsafe events. This paper investigates the abnormal flight recovery method in regional multiairport system based on risk control. The focus is to reschedule arrival-departure flights in real time with minimized delay time and risk probability. In this study, the risk about terminal area control and scene operation was considered in the analysis of the risk control model (RCM), which includes six key risk points: airspace control, flight conflict, ground service, apron support, ground control, and taxiing conflict. The mathematical model on flight recovery was constructed to solve minimized delay time and risk probability with MSINS (multistart algorithm with intelligent neighborhood selection). The data of a typical regional multiairport system in China were selected for experimental verification in order to compare the RCM with the traditional recovery model (TRM). The experimental results show that first, there are some hidden dangers in the traditional recovery methods of flight delay. Flight conflict and apron support are the risk points that need to be controlled most in the multiairport system. Secondly, for the effective solution with the shortest delay time, the RCM can reduce the overall operation risk of the system, but the flight delay time is a little longer. For the effective solution with the lowest risk probability, RCM can reduce the risk of system operation and the delay time of flights at the same time. Therefore, RCM can improve the security level of the system during abnormal flight recovery and ensure or even improve the recovery efficiency.
9
Zhou, Hang, and Xinxin Jiang. "Research on Arrival/Departure Scheduling of Flights on Multirunways Based on Genetic Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/851202.
Full textAbstract:
Aiming at the phenomenon of a large number of flight delays in the terminal area makes a reasonable scheduling for the approach and departure flights, which will minimize flight delay losses and improve runway utilization. This paper considered factors such as operating conditions and safety interval of multi runways; the maximum throughput and minimum flight delay losses as well as robustness were taken as objective functions; the model of optimization scheduling of approach and departure flights was established. Finally, the genetic algorithm was introduced to solve the model. The results showed that, in the program whose advance is not counted as a loss, its runway throughput is improved by 18.4%, the delay losses are reduced by 85.8%, and the robustness is increased by 20% compared with the results of FCFS (first come first served) algorithm, while, compared with the program whose advance is counted as a loss, the runway throughput is improved by 15.16%, flight delay losses are decreased by 75.64%, and the robustness is also increased by 20%. The algorithm can improve the efficiency and reduce delay losses effectively and reduce the workload of controllers, thereby improving economic results.
10
Dermadi, Yedi, Shinta Devi Lukitasari, and Annisaa Nurhayati. "Weather Analysis of Flight Delay at Husein Sastranegara Airport." ITEJ (Information Technology Engineering Journals) 4, no. 2 (December 31, 2019): 89–98. http://dx.doi.org/10.24235/itej.v4i2.31.
Full textAbstract:
Flight is an activity that is very vulnerable to weather conditions. The accuracy of weather information strongly supports flight activities. The effects of bad weather on flights include flight delays and flight cancellations. Based on data on flight delays from the Directorate General of Air Transportation of the Ministry of Transportation from January to March 2019 at Husein Sastranegara Airport, it is known that 20-30% of flight delays are caused by weather constraints. To estimate flight delays based on weather forecasts, weather data analysis is carried out to determine the type of weather that is endangering flights and causing flight delays. The analysis was carried out using the K-NN and Random Forest algorithms
11
Qalbi, Nurul, and Ahkam Jayadi. "Aspek Hukum Ganti Kerugian Keterlambatan Penerbangan (Flight Delay) Maskapai Penerbangan Komersial Indonesia." Alauddin Law Development Journal 2, no. 3 (November 19, 2020): 302–15. http://dx.doi.org/10.24252/aldev.v2i3.14642.
Full textAbstract:
AbstrakPengangkutan udara komersial di Indonesia pasca deregulasi penerbangan tahun 2000 yang dilakukan oleh pemerintah mengalami perkembangan pesat. Hal ini terlihat dari banyaknya perusahaan atau maskapai penerbangan saat ini. Padahal sebelumnya hanya 5 maskapai penerbangan yang melayani jasa penerbangan ke berbagai rute penerbangan. Sejalan dengan pesatnya perkembangan pengangkutan udara ternyata banyak menimbulkan masalah, salah satunya yakni masalah keterlambatan penerbangan (flight delay). Masalah keterlambatan penerbangan ini sangat merugikan penumpang. Karena kecepatan waktu dalam menempuh suatu wilayah menjadikan transportasi ini dipilih oleh kebanyakan orang. Sehubungan dengan masalah keterlambatan penerbangan maka diperlukan suatu aturan yang mengatur mengenai ganti kerugian yang disebabkan oleh keterlambatan penerbangan maskapai penerbangan komersial khususnya di Indonesia. Untuk mengetahui lebih lanjut dengan masalah ini, penulis melakukan penelitan. Dari hasil penelitian ini menunjukkan bahwa : pertama mengenai aturan tentang ganti kerugian keterlambatan (flight delay) penerbangan komersial di Indonesia diatur lebih detail dalam Peraturan Menteri Nomor 89 Tahun 2015 Tentang Penanganan Keterlambatan Penerbangan (Delay Management) Pada Badan Usaha Penerbangan Niaga Berjadwal Di Indonesia yang diamanatkan dalam Undang-undang RI No. 1 Tahun 2009 Tentang Penerbangan. Kedua, faktor-faktor yang menghambat pemberian ganti rugi dalam keterlambatan penerbangan (flight delay) yakni bisa dari Staff/pegawai serta berbagai sarana yang dimiliki maskapai penerbangan yang dapat menghambat pemberian ganti kerugian dan bisa juga dari penumpang itu sendiri karena kurang aktif dalam meminta hak-nya apabila terjadi keterlambatan penerbangan.Abstract Commercial air transportation in Indonesia after the deregulation of flights in 2000 by the government has undergone rapid development. It is able to be seen from the number of companies or airlines nowadays. Whereas there were only 5 airlines that served flight services for various flight routes. In line with the rapid development of air transportation, there are many problems arising, one of which is the problem of flight delays. This flight delay problem has been very detrimental for passengers due to the fact that the speed of time in traveling to an area makes this transportation chosen by most people. In connection with the problem of flight delays, we need a rule governing compensation caused by flight delays of commercial airlines, especially in Indonesia. To find out more about this problem, the authors conducted a research. The results of this study indicate that: firstly, it was concerning the rules on compensation for flight delays for commercial flights in Indonesia that are regulated in more detail in Ministerial Regulation No. 89 of 2015 concerning Handling of Delay Management in Scheduled Commercial Business Entities in Indonesia mandated in RI Law No. 1 of 2009 concerning Aviation. Secondly, the factors that hinder compensation in flight delays could be from staff / employees and various means owned by airlines that could inhibit compensation and also from passengers themselves because they were less active in asking for rights when there was a flight delay.Keywords: Ganti Rugi; Keterlambatan Penerbangan; Ganti Kerugian Maskapai Penerbangan
12
Stone, Matthew J. "Impact of delays and cancellations on travel from small community airports." Tourism and Hospitality Research 18, no. 2 (March 15, 2016): 214–28. http://dx.doi.org/10.1177/1467358416637252.
Full textAbstract:
Commercial air service is important to residents and tourists in small communities, but small communities have seen a reduction (and sometimes elimination) of commercial air service. Anecdotal evidence suggests that flight interruptions (delays and cancellations) negatively impact these airports, as local residents drive to nearby hubs instead of flying from the local airport. This study looks at the impact of delays and cancellations by considering their effect on an entire travel itinerary. This study investigates 207 itineraries from eight small American airports to 11 hub airports. On average, passengers on about one in six (16.4%) connecting itineraries would face a missed connection due to delay or cancellation. In addition, delays on origin flights magnify across connections. An average delay on the initial flight (68 min) would lead to an average arrival delay of 90 min across all itineraries, while a cancellation would lead to an arrival delay of over 10 h. This study also introduces a measure of overall lateness that combines delay and cancellation percentages with final destination arrival times across nonstop and connecting itineraries. These flight interruptions may have consequences on maintaining air service, which affects community residents and potential visitors.
13
Xing, Zhi Wei, Jiang Yu, and Hong Yue Lu. "Optimal Control of Flight Delays Allocation at Airport." Applied Mechanics and Materials 513-517 (February 2014): 4494–98. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4494.
Full textAbstract:
Aiming at improving the unreasonable situation of flight delay allocation, an optimization model which contains two cases was proposed. One of the cases is to transfer all the delay to several delayed flights to avoid delay spread, which can increase the flight punctuality rate to ensure the departure of the majority flights. Oppositely, the other is to balance the delay losses of airlines, as well as the passengers, for realizing the fairness. According to the latest data from an airport in western China, the model was verified with genetic algorithm, which indicates that the model not only decrease the total delay losses, but also optimize the flight delay allocation which achieves the initial goal.
14
C., P. "Ariane flight delay." Nature 330, no. 6145 (November 1987): 197. http://dx.doi.org/10.1038/330197d0.
Full text
15
Zoutendijk, Micha, and Mihaela Mitici. "Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem." Aerospace 8, no. 6 (May 28, 2021): 152. http://dx.doi.org/10.3390/aerospace8060152.
Full textAbstract:
The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.
16
Alla, Hajar, Lahcen Moumoun, and Youssef Balouki. "A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction." Scientific Programming 2021 (June 14, 2021): 1–12. http://dx.doi.org/10.1155/2021/5558918.
Full textAbstract:
Flight delay is the most common preoccupation of aviation stakeholders around the world. Airlines, which suffer from a monetary and customer loyalty loss, are the most affected. Various studies have attempted to analyze and solve flight delays using machine learning algorithms. This research aims to predict flights’ arrival delay using Artificial Neural Network (ANN). We applied a MultiLayer Perceptron (MLP) to train and test our data. Two approaches have been adopted in our work. In the first one, we used historical flight data extracted from Bureau of Transportation Statistics (BTS). The second approach improves the efficiency of the model by applying selective-data training. It consists of selecting only most relevant instances from the training dataset which are delayed flights. According to BTS, a flight whose difference between scheduled and actual arrival times is 15 minutes or greater is considered delayed. Departure delays and flight distance proved to be very contributive to flight delays. An adjusted and optimized hyperparameters using grid search technique helped us choose the right architecture of the network and have a better accuracy and less error than the existing literature. The results of both traditional and selective training were compared. The efficiency and time complexity of the second method are compared against those of the traditional training procedure. The neural network MLP was able to predict flight arrival delay with a coefficient of determination R 2 of 0.9048, and the selective procedure achieved a time saving and a better R 2 score of 0.9560. To enhance the reliability of the proposed method, the performance of the MLP was compared with that of Gradient Boosting (GB) and Decision Trees (DT). The result is that the MLP outperformed all existing benchmark methods.
17
Shi, Tongyu, Jinghan Lai, Runping Gu, and Zhiqiang Wei. "An Improved Artificial Neural Network Model for Flights Delay Prediction." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 08 (March 24, 2021): 2159027. http://dx.doi.org/10.1142/s0218001421590278.
Full textAbstract:
With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance.
18
Kammoun, Mohamed Ali, Sadok Turki, and Nidhal Rezg. "Optimization of Flight Rescheduling Problem under Carbon Tax." Sustainability 12, no. 14 (July 10, 2020): 5576. http://dx.doi.org/10.3390/su12145576.
Full textAbstract:
The flight rescheduling problem is one of the major challenges of air traffic issue. Unforeseen bad weather conditions stimulate air traffic congestion and make the initial scheduling infeasible, resulting in significant economic losses for passengers and airlines. Furthermore, due to rigorous environmental legislations, flight rescheduling becomes a more complicated problem, as it has to deal with flight delays on the one hand, and carbon emissions on the other hand. In this paper, we address the flight rescheduling problem with an environmental requirement subject to the air capacity limitation due to bad weather conditions. A new strategy is proposed to minimize the disruption effects on planned flights, which adopted ground delay, longer route change, flight cancellation, as well speed adjustment to arrive at a scheduled time. Firstly, the objective of this study is to determine the economical flights plan in line with the new available air capacity. Secondly, by considering the environmental impact of the kerosene consumption, we illustrate the contribution of an economical decision to aircraft emissions. Experiment results are provided to show the efficiency of the proposed strategies and genetic algorithm as the used optimization method. Furthermore, the impacts of carbon tax and cost of arrival delay on the flights carbon emissions are studied.
19
Wu, Weiwei, Haoyu Zhang, Tao Feng, and Frank Witlox. "A Network Modelling Approach to Flight Delay Propagation: Some Empirical Evidence from China." Sustainability 11, no. 16 (August 15, 2019): 4408. http://dx.doi.org/10.3390/su11164408.
Full textAbstract:
This paper examines flight delay propagation in air transport networks. Delays add to additional costs, inefficiencies, and unsustainable development. An integrated flight-based susceptible-infected-susceptible (FSIS) model was developed to analyse the flight delay process from a network-based perspective. The probability of flight delay propagation was determined using a translog model. The model was applied to an airline network consisting of thirty-three routes involving three airlines. The results show that the propagation probability is network-related and varies across different routes. The variation is related to the flight frequencies at airports, route distances, scheduled buffer times, and the propagated delay time. Whereas buffer time has a greater impact on smaller airports, flight movement has a greater impact on larger airports. Having a better understanding of how delays happen can help the development of strategies to avoid them. This will lead to less costs, higher efficiencies, and more sustainable airport and airline development.
20
Zhixing, Tang, Huang Shan, and Han Songchen. "Recent Progress about Flight Delay under Complex Network." Complexity 2021 (April 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/5513093.
Full textAbstract:
Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important nodes/edges. And, we made a clarification about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. (1) Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. (2) Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. (3) It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. (4) Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed.
21
Lewis, Bridget A., Valerie J. Gawron, Ehsan Esmaeilzadeh, Ralf H. Mayer, Felipe Moreno-Hines, Neil Nerwich, and Paulo M. Alves. "Data-Driven Estimation of the Impact of Diversions Due to In-Flight Medical Emergencies on Flight Delay and Aircraft Operating Costs." Aerospace Medicine and Human Performance 92, no. 2 (February 1, 2021): 99–105. http://dx.doi.org/10.3357/amhp.5720.2021.
Full textAbstract:
INTRODUCTION: In-flight medical emergencies (IFMEs) average 1 of every 604 flights and are expected to increase as the population ages and air travel increases. Flight diversions, or the rerouting of a flight to an alternate destination, occur in 2 to 13% of IFME cases, but may or may not be necessary as determined after the fact. Estimating the effect of IFME diversions compared to nonmedical diversions can be expected to improve our understanding of their impact and allow for more appropriate decision making during IFMEs.METHODS: The current study matched multiple disparate datasets, including medical data, flight plan and track data, passenger statistics, and financial data. Chi-squared analysis and independent samples t-tests compared diversion delays and costs metrics between flights diverted for medical vs. nonmedical reasons. Data were restricted to domestic flights between 1/1/2018 and 6/30/2019.RESULTS: Over 70% of diverted flights recover (continue on to their intended destination after diverting); however, flights diverted due to IFMEs recover more often and more quickly than do flights diverted for nonmedical reasons. IFME diversions introduce less delay overall and cost less in terms of direct operating costs and passenger value of time (averaging around 38,000) than do flights diverted for nonmedical reasons.DISCUSSION: Flights diverted due to IFMEs appear to have less impact overall than do flights diverted for nonmedical reasons. However, the lack of information related to costs for nonrecovered flights and the decision factors involved during nonmedical diversions hinders our ability to offer further insights.Lewis BA, Gawron VJ, Esmaeilzadeh E, Mayer RH, Moreno-Hines F, Nerwich N, Alves PM. Data-driven estimation of the impact of diversions due to in-flight medical emergencies on flight delay and aircraft operating costs. Aerosp Med Hum Perform. 2021; 92(2):99105.
22
Jiang, Yu, Zhaolong Xu, Xinxing Xu, Zhihua Liao, and Yuxiao Luo. "A Schedule Optimization Model on Multirunway Based on Ant Colony Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/368208.
Full textAbstract:
In order to make full use of the slot of runway, reduce flight delay, and ensure fairness among airlines, a schedule optimization model for arrival-departure flights is established in the paper. The total delay cost and fairness among airlines are two objective functions. The ant colony algorithm is adopted to solve this problem and the result is more efficient and reasonable when compared with FCFS (first come first served) strategy. Optimization results show that the flight delay and fair deviation are decreased by 42.22% and 38.64%, respectively. Therefore, the optimization model makes great significance in reducing flight delay and improving the fairness among all airlines.
23
Zámková, Martina, Martin Prokop, and Radek Stolín. "Factors Influencing Flight Delays of a European Airline." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, no. 5 (2017): 1799–807. http://dx.doi.org/10.11118/actaun201765051799.
Full textAbstract:
The main aim of the paper was to investigate factors influencing flight delays of a European airline. Besides the identification and analysis of those factors the paper offers possible suggestions on how to eliminate the delays. The research is based on data acquired over the period of time spanning from June to September in 2008 – 2014. Analysis of contingency tables, including Pearson’s chi‑squared test, has been used for data processing. The dependencies have been presented in graphical form by using correspondence maps. The proportion of delayed flights reaches approx. 50 % during nearly the entire monitored period only in September the proportion drops to 45 %. Flight delays are most frequently caused by delays of previous flights of the same plane. These previous delayed flights are the main culprit of long delays and the frequency of delay occurrence caused by this reason increases significantly during the day. Longer delays of flights appear also due to technical maintenance or aircraft defects. On the contrary other factors such as operational control and crew duty norms, air traffic control and airport limitations tend to cause rather shorter delays of flights with the air traffic control encountering more problems with coordination of flights in the early morning. The supply and service companies also manage to eliminate long delays.
24
Wang, Huawei, and Jun Gao. "Bayesian Network Assessment Method for Civil Aviation Safety Based on Flight Delays." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/594187.
Full textAbstract:
Flight delays and safety are the principal contradictions in the sound development of civil aviation. Flight delays often come up and induce civil aviation safety risk simultaneously. Based on flight delays, the random characteristics of civil aviation safety risk are analyzed. Flight delays have been deemed to a potential safety hazard. The change rules and characteristics of civil aviation safety risk based on flight delays have been analyzed. Bayesian networks (BN) have been used to build the aviation operation safety assessment model based on flight delay. The structure and parameters learning of the model have been researched. By using BN model, some airline in China has been selected to assess safety risk of civil aviation. The civil aviation safety risk of BN model has been assessed by GeNIe software. The research results show that flight delay, which increases the safety risk of civil aviation, can be seen as incremental safety risk. The effectiveness and correctness of the model have been tested and verified.
25
Wang, Yanjun, Hongfeng Zheng, Fan Wu, Jun Chen, and Mark Hansen. "A Comparative Study on Flight Delay Networks of the USA and China." Journal of Advanced Transportation 2020 (June 2, 2020): 1–11. http://dx.doi.org/10.1155/2020/1369591.
Full textAbstract:
Recent studies have characterized the structures of air transport network in different countries and regions using complex network metrics. These studies coincided with the trend of increasingly available large empirical flight datasets that enable researchers to investigate the dynamics of the system, such as the propagation of flight delay. However, linking network structure with network dynamics remains a challenging task. In this paper, we proposed a method to construct flight delay networks from operational data. We provided a detailed comparison of the key structural properties of the flight delay networks in the United States and China. The comparisons of betweenness centrality of delay networks and flight networks show the advantage of the proposed method. We further found that airports in similar geographical locations do exhibit similar delay patterns in both countries. To explore the underlying mechanisms, the Multifractal Detrended Fluctuation Analysis (MF-DFA) is applied to the flights’ delay time series at both the airport level and network level. Singularity spectra analyses reveal the fundamental characteristics of the airport systems and air transportation system. Our findings contribute to the understanding of structure and dynamics of air transportation systems.
26
Legiman, Febriana Samsi, Luh Putu Sudini, and I. Nyoman Sutama. "Tanggung Jawab Keperdataan dalam Pengangkutan Udara atas Keterlambatan Jadwal Penerbangan." Jurnal Preferensi Hukum 1, no. 2 (September 15, 2020): 150–53. http://dx.doi.org/10.22225/jph.1.2.2383.150-153.
Full textAbstract:
Aviation is a vehicle that is part of transportation that has the ability to move quickly in time, which is able to lift goods and people from one area to another using high capabilities, and requires high safety and security interests in order to help create distribution that is good. steady and smooth. This article aims to find out the civil liability for delays in flights that harming passengers and compensation provided by the carrier to passengers in the event of delay due to default. The type of research used in the writing of this law is normative research. The approach used is the approach of the Act. In practice, airlines are responsible for any losses suffered by passengers in the event of flight delays / delays in the performance of airline duties in accordance with the principle of responsibility based on the element of error. Forms of airline liability against loss suffered by passengers in the event of a flight delays / delay in the implementation of the duties of airlines in the form of burdened return ticket, food and beverage and move passengers to the next flight.
27
Lugovoi, V. G., A. V. Sorokin, and O. V. Shilov. "FUEL PLANNING PROBLEMS FOR FLIGHTS, PLANNED VIA STANDARD ARRIVAL ROUTES (STAR) WITH DELAY LEGS." Civil Aviation High TECHNOLOGIES 22, no. 2 (April 24, 2019): 28–37. http://dx.doi.org/10.26467/2079-0619-2019-22-2-28-37.
Full textAbstract:
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.
28
Rumani, Daniel, Hadi Prayitno, and Intan Rizka Subandi. "Pengaruh Pengoperasian Wide-Body Aircraft Terhadap Kelancaran Arus Lalu Lintas Penerbangan di Makassar Air Traffic Service Center." SPROCKET JOURNAL OF MECHANICAL ENGINEERING 2, no. 1 (November 5, 2020): 8–12. http://dx.doi.org/10.36655/sproket.v2i1.459.
Full textAbstract:
This study aims to obtain a description of whether there is an influence between the movement of wide-body aircraft both departure and arrival on flight delays as a measure of the smooth flow of flight traffic at the Makassar Air Traffic Service Center. The research concludes that there is a positive relationship between the operation of wide-body aircraft and flight delays of 0.870. The positive correlation indicates that the more wide-body aircraft operations, the flight delay will increase, which if there is an increase in flight delays, the smooth flow of flight traffic will decrease. The coefficient of determination is 75%, which means that the effect of the operation of wide-body aircraft on flight delays is 75% and the rest is determined by other factors not included in the research section. The equation obtained from the regression test is Y = -5,679 + 1,872 X. So, if the operation of wide-body aircraft increases by 1, the flight delay will increase by 1,872 at a constant of -5,679.
29
McCarthy, Nicholas, Mohammad Karzand, and Freddy Lecue. "Amsterdam to Dublin Eventually Delayed? LSTM and Transfer Learning for Predicting Delays of Low Cost Airlines." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9541–46. http://dx.doi.org/10.1609/aaai.v33i01.33019541.
Full textAbstract:
Flight delays impact airlines, airports and passengers. Delay prediction is crucial during the decision-making process for all players in commercial aviation, and in particular for airlines to meet their on-time performance objectives. Although many machine learning approaches have been experimented with, they fail in (i) predicting delays in minutes with low errors (less than 15 minutes), (ii) being applied to small carriers i.e., low cost companies characterized by a small amount of data. This work presents a Long Short-Term Memory (LSTM) approach to predicting flight delay, modeled as a sequence of flights across multiple airports for a particular aircraft throughout the day. We then suggest a transfer learning approach between heterogeneous feature spaces to train a prediction model for a given smaller airline using the data from another larger airline. Our approach is demonstrated to be robust and accurate for low cost airlines in Europe.
30
Algarin Ballesteros, Jose A., and Nathan M. Hitchens. "Meteorological Factors Affecting Airport Operations during the Winter Season in the Midwest." Weather, Climate, and Society 10, no. 2 (March 23, 2018): 307–22. http://dx.doi.org/10.1175/wcas-d-17-0054.1.
Full textAbstract:
Abstract During the coldest months of the year, weather systems bring a variety of winter weather to most of the continental United States in the form of snow, sleet, and freezing rain, which along with strong winds, low clouds, and reduced visibilities may create dangerous conditions. These weather conditions can result in major disruptions in air travel, leading to delays and cancellations of hundreds or thousands of flights, thus affecting the plans of millions of travelers. To assess the specific meteorological factors that prompt flight delays and cancellations in the Midwest region of the United States during wintertime, a comprehensive study was performed on nine of the largest airports (by passenger boardings) in the area. Flight delay and cancellation data from 11 winter seasons (2005–06 to 2015–16) were collected from the Bureau of Transportation Statistics (BTS) and analyzed along with climatological data from the National Centers for Environmental Information (NCEI). A classification scheme was developed, and each flight was categorized according to the meteorological factor that could have prompted its delay. The results of the study revealed that visibility was the main meteorological factor affecting midwestern airports, with low ceilings as a close second. Blizzards were the main cause for flight cancellations.
31
Esmaeilzadeh, Ehsan, and Seyedmirsajad Mokhtarimousavi. "Machine Learning Approach for Flight Departure Delay Prediction and Analysis." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (July 2, 2020): 145–59. http://dx.doi.org/10.1177/0361198120930014.
Full textAbstract:
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.
32
Zhang, Mingyuan, Xuting Zhou, Yu Zhang, Lijun Sun, Ming Dun, Wenbo Du, and Xianbin Cao. "Propagation Index on Airport Delays." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 8 (April 28, 2019): 536–43. http://dx.doi.org/10.1177/0361198119844240.
Full textAbstract:
This paper explores the propagation effect of flight delays among airports in the aviation system and proposes a new measure, the propagation index, to effectively analyze the interrelationship among airports in relation to flight delays. This index quantifies the effect of delay propagation by measuring the causality among delay time series. To assess the effectiveness of the proposed index on airport delays, three neural network-based regression models are built. The comparative experiments demonstrate that the propagation index proposed is highly correlated with observed airport delays.
33
Rosenow, Judith, Philipp Michling, Michael Schultz, and Jörn Schönberger. "Evaluation of Strategies to Reduce the Cost Impacts of Flight Delays on Total Network Costs." Aerospace 7, no. 11 (November 18, 2020): 165. http://dx.doi.org/10.3390/aerospace7110165.
Full textAbstract:
Competitive price pressure and economic cost pressure constantly force airlines to improve their optimization strategies. Besides predictable operational costs, delay costs are a significant cost driver for airlines. Especially reactionary delay costs can endanger the profitability of such a company. These time-dependent costs depend on the number of sensitive transfer passengers. This cost component is represented by the number of missed flights and the connectivity of onward flights, i.e., the offer of alternative flight connections. The airline has several options to compensate for reactionary delays, for example, by increasing cruising speeds, shortening turnaround times, rebookings and cancellations. The effects of these options on the cost balance of airline total operating costs have been examined in detail, considering a flight-specific number of transfer passengers. The results have been applied to a 24-h rotation schedule of a large German hub airport. We found, that the fast turnaround and increasing cruise speed are the most effective strategies to compensate for passenger-specific delay costs. The results could be used in a multi-criteria trajectory optimization to find a balance between environmentally-driven and cost-index-driven detours and speed adjustments.
34
Yi, Jia, Honghai Zhang, Hao Liu, Gang Zhong, and Guiyi Li. "Flight Delay Classification Prediction Based on Stacking Algorithm." Journal of Advanced Transportation 2021 (August 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/4292778.
Full textAbstract:
With the development of civil aviation, the number of flights keeps increasing and the flight delay has become a serious issue and even tends to normality. This paper aims to prove that Stacking algorithm has advantages in airport flight delay prediction, especially for the algorithm selection problem of machine learning technology. In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. The second-level learner is Logistic Regression. To verify the effectiveness of the proposed method, comparative experiments are carried out based on Boston Logan International Airport flight datasets from January to December 2019. Multiple indexes are used to comprehensively evaluate the prediction results, such as Accuracy, Precision, Recall, F1 Score, ROC curve, and AUC Score. The results show that the Stacking algorithm not only could improve the prediction accuracy but also maintains great stability.
35
Setiawan, Indra, Suharto Abdul Majid, and Yuliantini Yuliantini. "AIRPORT FACTOR IN FLIGHT DELAYS IN INDONESIA." JURNAL MANAJEMEN TRANSPORTASI DAN LOGISTIK 2, no. 3 (July 19, 2017): 365. http://dx.doi.org/10.25292/j.mtl.v2i3.115.
Full textAbstract:
Airport is a vital subsystem in the air transport system which has an important and strategic role in smoothening air traffic, so that an airport must be designed to be always ready in all situations and conditions to serve airline flights securely, orderly, smoothly, and quickly. The frequent delay faced by Indonesian domestic scheduled airlines is not fully caused by internal factor of the companies. Instead, it can be caused by the airport factor. The problems faced by airports in Indonesia which have impacts on the performance of flight punctuality among other things are capacity, slot time, quality of flight navigation devices, accessibility, professionalism of airport and ATC human resources, infrastructure, facilities, equipment, and the human resources handling security and safety of flights in airport. This article recommends that an in-depth study should be carried out to map the interrelations between the airport factor in the flight delay related to facilities integration and the airport authority in the flight activities in Indonesia.
36
Chen, Jiu Sheng, and Xiao Yu Zhang. "Modeling of Flight Arrival Scheduling Based on Fuzzy Programming." Applied Mechanics and Materials 313-314 (March 2013): 995–98. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.995.
Full textAbstract:
The issue of flight arrival in civil airport is a typical problem of discrete event dynamic system. IN Time is a random variable. According to the characteristics of arrival flights, flight delay cost as objective function, the fuzzy model for scheduling arrival flights is established. In the case of growing air traffic, the model is a better opinion for scientific optimum ordering matter in air traffic control system. It can improve flight operation on time.
37
Du, Jing, Jin Fu Zhu, Wei Wei Wu, and Shun Zhi Xu. "Research on Flight Delay Based on Adaptive Agent Digraph." Applied Mechanics and Materials 496-500 (January 2014): 2942–45. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2942.
Full textAbstract:
Flight delay is a serious problem which many airlines encounter. This paper studies on flight delay problem based on the thought of adaptive agent digraph,and then does the simulation analysis. The indicator of entropy is proposed to measure the uncertainty of flight delay occurrence. The results show that if entropy is not zero, flight delay occurs,and if entropy increases, the possibility of flight delay increases as well as its spread effect. So using entropy as the indicator to reflect the situation of flight delay is feasible.
38
Wang, Huawei, Yuxiao Luo, and Zhijian Shi. "Real-Time Gate Reassignment Based on Flight Delay Feature in Hub Airport." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/646241.
Full textAbstract:
Appropriate gate reassignment is crucially important in efficiency improvement on airport sources and service quality of travelers. The paper divides delay flight into certain delay time flight and uncertain delay time flight based on flight delay feature. The main objective functions of model are to minimize the disturbance led by gate reassignment in the case of certain delay time flight and uncertain delay time flight, respectively. Another objective function of model is to build penalty function when the gate reassignment of certain delay time flight influences uncertain delay time flight. Ant colony algorithm (ACO) is presented to simulate and verify the effectiveness of the model. The comparison between simulation result and artificial assignment shows that the result coming from ACO is obvious prior to the result coming from artificial assignment. The maximum disturbance of gate assignment is decreased by 13.64%, and the operation time of ACO is 118 s. The results show that the strategy of gate reassignment is feasible and effective.
39
Liu, Junqiang. "Flights Assignment Model of Multiple Airports Based on Game Theory and CDM Mechanism." Mathematical Problems in Engineering 2020 (January 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/2569280.
Full textAbstract:
To minimize delay cost of flights in multiple airports, this paper studied flights assignment problem under delay conditions. By considering the delay cost, airport capacity, and the slot exchange between airlines, this paper proposed a novel assignment model based on game theory and CDM mechanism. An improved ant colony algorithm was proposed to solve the flight assignment problem. The case studies showed that the proposed model can optimize the minimum delay cost between airlines under multiairport capacity constraints. The performance of proposed method was better than that of traditional non-slot-exchange method.
40
Lin, Huai-Ti, Ivo G. Ros, and Andrew A. Biewener. "Through the eyes of a bird: modelling visually guided obstacle flight." Journal of The Royal Society Interface 11, no. 96 (July 6, 2014): 20140239. http://dx.doi.org/10.1098/rsif.2014.0239.
Full textAbstract:
Various flight navigation strategies for birds have been identified at the large spatial scales of migratory and homing behaviours. However, relatively little is known about close-range obstacle negotiation through cluttered environments. To examine obstacle flight guidance, we tracked pigeons ( Columba livia ) flying through an artificial forest of vertical poles. Interestingly, pigeons adjusted their flight path only approximately 1.5 m from the forest entry, suggesting a reactive mode of path planning. Combining flight trajectories with obstacle pole positions, we reconstructed the visual experience of the pigeons throughout obstacle flights. Assuming proportional–derivative control with a constant delay, we searched the relevant parameter space of steering gains and visuomotor delays that best explained the observed steering. We found that a pigeon's steering resembles proportional control driven by the error angle between the flight direction and the desired opening, or gap, between obstacles. Using this pigeon steering controller, we simulated obstacle flights and showed that pigeons do not simply steer to the nearest opening in the direction of flight or destination. Pigeons bias their flight direction towards larger visual gaps when making fast steering decisions. The proposed behavioural modelling method converts the obstacle avoidance behaviour into a (piecewise) target-aiming behaviour, which is better defined and understood. This study demonstrates how such an approach decomposes open-loop free-flight behaviours into components that can be independently evaluated.
41
Baspinar, B., and E. Koyuncu. "A Data-Driven Air Transportation Delay Propagation Model Using Epidemic Process Models." International Journal of Aerospace Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4836260.
Full textAbstract:
In air transport network management, in addition to defining the performance behavior of the system’s components, identification of their interaction dynamics is a delicate issue in both strategic and tactical decision-making process so as to decide which elements of the system are “controlled” and how. This paper introduces a novel delay propagation model utilizing epidemic spreading process, which enables the definition of novel performance indicators and interaction rates of the elements of the air transportation network. In order to understand the behavior of the delay propagation over the network at different levels, we have constructed two different data-driven epidemic models approximating the dynamics of the system: (a) flight-based epidemic model and (b) airport-based epidemic model. The flight-based epidemic model utilizing SIS epidemic model focuses on the individual flights where each flight can be in susceptible or infected states. The airport-centric epidemic model, in addition to the flight-to-flight interactions, allows us to define the collective behavior of the airports, which are modeled as metapopulations. In network model construction, we have utilized historical flight-track data of Europe and performed analysis for certain days involving certain disturbances. Through this effort, we have validated the proposed delay propagation models under disruptive events.
42
Elzinga, Michael J., William B. Dickson, and Michael H. Dickinson. "The influence of sensory delay on the yaw dynamics of a flapping insect." Journal of The Royal Society Interface 9, no. 72 (December 21, 2011): 1685–96. http://dx.doi.org/10.1098/rsif.2011.0699.
Full textAbstract:
In closed-loop systems, sensor feedback delays may have disastrous implications for performance and stability. Flies have evolved multiple specializations to reduce this latency, but the fastest feedback during flight involves a delay that is still significant on the timescale of body dynamics. We explored the effect of sensor delay on flight stability and performance for yaw turns using a dynamically scaled robotic model of the fruitfly, Drosophila . The robot was equipped with a real-time feedback system that performed active turns in response to measured torque about the functional yaw axis. We performed system response experiments for a proportional controller in yaw velocity for a range of feedback delays, similar in dimensionless timescale to those experienced by a fly. The results show a fundamental trade-off between sensor delay and permissible feedback gain, and suggest that fast mechanosensory feedback in flies, and most probably in other insects, provide a source of active damping which compliments that contributed by passive effects. Presented in the context of these findings, a control architecture whereby a haltere-mediated inner-loop proportional controller provides damping for slower visually mediated feedback is consistent with tethered-flight measurements, free-flight observations and engineering design principles.
43
Senda, Kei. "OS1-11 FLIGHT CONTROL OF A FLAPPING BUTTERFLY CONSIDERING TIME DELAY(OS1: Bio-inspired Flight System Biomechanics II)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2015.8 (2015): 72. http://dx.doi.org/10.1299/jsmeapbio.2015.8.72.
Full text
44
SELTZER, RICHARD. "Doubts about adhesive delay shuttle flight." Chemical & Engineering News 74, no. 30 (July 22, 1996): 11. http://dx.doi.org/10.1021/cen-v074n030.p011.
Full text
45
Fernandes, Nuno, Sérgio Moro, Carlos J. Costa, and Manuela Aparício. "Factors influencing charter flight departure delay." Research in Transportation Business & Management 34 (March 2020): 100413. http://dx.doi.org/10.1016/j.rtbm.2019.100413.
Full text
46
Kalliguddi, Anish M., and Aera K. Leboulluec. "Predictive Modeling of Aircraft Flight Delay." Universal Journal of Management 5, no. 10 (October 2017): 485–91. http://dx.doi.org/10.13189/ujm.2017.051003.
Full text
47
Kazemi Asfe, Meysam, Majid Jangi Zehi, Mohammad Nabi Shahiki Tash, and Noor Mohammad Yaghoubi. "Ranking different factors influencing flight delay." Management Science Letters 4, no. 7 (2014): 1397–400. http://dx.doi.org/10.5267/j.msl.2014.6.030.
Full text
48
Liu, Jun-qiang, Ma-lan Zhang, Peng-chao Chen, Ji-wei Xie, and Hong-fu Zuo. "An Integrative Approach with Sequential Game to Real-Time Gate Assignment under CDM Mechanism." Mathematical Problems in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/143501.
Full textAbstract:
This paper focuses on real-time airport gate assignment problem when small-scale or medium- to large-scale flight delays occur. Taking into account the collaborative decision making (CDM) of the airlines and the airport, as well as the interests of multiagent (airlines, airports, and passengers), especially those influenced by flight banks, slot assignment and gate assignment are integrated into mixed set programming (MSP), and a real-time gate assignment model is built and solved through MSP coupled with sequential game. By this approach, the delay costs of multiagent can be minimized simultaneously; the fuel consumption of each airline can be basically equalized; the computation time can be significantly saved by sequential game; most importantly, the collaboration of the airlines and the airport is achieved so that the transferring cost caused by the delay of flight banks can be decreased as much as possible. A case study on small-scale flight delays verifies that the proposed approach is economical, robust, timesaving, and collaborative. A comparison of the traditional staged method and the proposed approach under medium- to large-scale flight delays proves that the integrative method is much more economical and timesaving than the traditional staged method.
49
Wang, Danyi, Lance Sherry, Ning Xu, and Melanie Larson. "Statistical Comparison of Passenger Trip Delay and Flight Delay Metrics." Transportation Research Record: Journal of the Transportation Research Board 2052, no. 1 (January 2008): 72–78. http://dx.doi.org/10.3141/2052-09.
Full text
50
Çetek, F. Aybek, Y. M. Kantar, and A. Cavcar. "A regression model for terminal airspace delays." Aeronautical Journal 121, no. 1239 (May 2017): 680–92. http://dx.doi.org/10.1017/aer.2017.19.
Full textAbstract:
ABSTRACTAir Traffic Management (ATM) research generally focuses on achieving a safer, more effective and economical air traffic system. The current airspace system has become increasingly strained as the demand for air travel has steadily grown. Innovative, proactive and multi-disciplinary approaches to research are needed to solve flight congestion and delays as a consequence of this rapid growth. As a result of this growth, air traffic flow becomes more complex, especially in Terminal Airspaces (TMA) where climb and descent manoeuvres of departing and arriving flights take place around airports. As air traffic demand exceeds the capacity in a TMA, the resultant congestion leads to delays that spread all over the system. Therefore, the reduction of delays is critical for airspace designers to increase customer satisfaction and the perception of service quality. Numerous studies have been conducted to reduce delays within TMAs. This research focuses on defining the causes of delays quantitatively through statistical analysis. The first step was to create a fast-time simulation model of sample airspace for collecting delay data. After building up this model using the SIMMOD fast-time ATM simulation tool, simulation experiments were run to produce various traffic scenarios and to generate traffic delay data. The number of airports, entry points, fixes and flight operations in airspace and the probability of wide-body aircraft were considered as independent variables. The correlations between the considered variables were analysed, and the total delay data was modelled using a linear regression model. The findings of regression model present a statistical approach for airspace designers and air traffic flow planners.