Airport Delays Research Articles

Flight Schedule Optimization Considering Fine-Grained Configuration of Slot Coordination Parameters

In response to the rapid growth of air passenger and cargo transportation services and the sharp increase in congestion at various airports, it is necessary to optimize the allocation of flight schedules. On the basis of reducing the total airport delay time and ensuring the total deviation of flight schedules applied by airlines, it is necessary to consider finely configuring flight schedules with slot coordination parameters, introducing a 5 min slot coordination parameter, and optimizing airport flight schedules in different time periods. This article considers factors such as flight schedule uniqueness, corridor flow restrictions, and time adjustment range limitations to establish a three-objective flight-schedule refinement configuration model, which is solved using the NSGA-II algorithm based on the entropy weight method. Taking Beijing Capital International Airport as an example, the optimized results show that the total flight delay was reduced from 4130 min to 1142 min, and the original delay of 389 flights was reduced to 283 flights. Therefore, flight schedule optimization considering the fine-grained configuration of slot coordination parameters can effectively reduce airport delays, fully utilize time resources, and reduce waste of time slot resources.

Research on Changes in Travel Insurance Premiums Driven by Climate Change: A Case Study of Hong Kong Region

This research aims to study the impacts of climate change on worldwide general travel insurance in Hong Kong and to provide a scientific basis for premium pricing strategies in a climate-driven context. The study adopts both qualitative and quantitative research methods to reveal the relationship between climate change and travel insurance. The qualitative analysis part constructs a theoretical framework through literature review. Taking into account the current state of research, the impact of climate change on the demand for travel insurance as well as the challenges and opportunities of premium pricing strategies are discussed in depth. The quantitative analysis section collected data on flights at the Hong Kong International Airport (departures, delays, and cancellations) and travel insurance claim occurrence. The weather conditions were also checked through the weather website to analyze the correlation between climate factors and insurance claims. The findings indicated that climate change has a significant impact on the demand for travel insurance, especially during the prolonged rainy season in Hong Kong, when extreme weather events lead to an increased risk of cancellation or delay of travel plans. Meanwhile, the quantitative analysis results demonstrated a significant correlation between weather factors and flight status and insurance occurrence, which provided empirical evidence for risk assessment and premium pricing of worldwide travel insurance in Hong Kong.

Scenario-based robust reachability analysis for networked airport delay dynamics

This work introduces a novel scheme to represent delay dynamics in airport networks. A key feature of the proposed scheme is that uncertainty affecting network operation is accounted for via a robust reachability analysis approach. In particular, the dynamical system is adopted to describe the propagation of outbound and inbound delays between airport nodes. A probabilistic reach set for this networked system is formulated as a chance-constrained optimization program. Then, we resort to a data-driven randomized computation method called scenariooptimization , where the chance constraint is replaced by a set of scenario-based deterministic constraints. Besides, the robustness of the reach set is explicitly derived through a posterior algorithm. Moreover, we conduct the extensive experiments on a dataset of real-world airport delays in China and provide some interesting results. Additionally, the case study at a refined scenario level shows the potential of the proposed approach in application.

Multi-Step Regression Network With Attention Fusion for Airport Delay Prediction

Multi-Step Regression Network With Attention Fusion for Airport Delay Prediction

Integrating multiple data sources for improved flight delay prediction using explainable machine learning

Flight delays negatively impact costs, customer satisfaction, and revenue in the aviation industry. As a result, it is critical to identify the factors that cause flight delays for each airport, as they can vary depending on various attributes associated with their operations.This study proposes an explainable artificial intelligence (xAI) methodology for identifying the features that affect airport delays by integrating data from multiple sources and implementing explainable artificial intelligence. The methodology incorporates operational data, airport information, geographic data, and weather data combined and used to train a series of machine learning models. Furthermore, the SHAP and Sobol techniques are used to thoroughly analyze the features that influence flight delays for the specific case of the airport in Santiago, Chile.The results show that a linear discriminant analysis model is best suited for predicting flight delays in this specific case study, and the features that have the most significant impact on delays are the international flight status, average temperature at the destination airport, wind speed, and average temperature at Santiago airport.The proposed methodology could be applied by airlines that can collect data from multiple sources and conduct similar investigations, leading to the development of a decision support system to make better-informed decisions and reduce the impact of flight delays.

A Monte Carlo approach for capacity and delay analyses of multiple interacting airports in Istanbul metroplex

AbstractThe Istanbul metroplex airspace, home to Atatürk (LTBA), Sabiha Gökçen (LTFJ), and Istanbul (LTFM) international airports, is a critical hub for international travel, trade and commerce between Europe and Asia. The high air traffic volume and the proximity of multiple airports make air traffic management (ATM) a significant challenge. To better manage this complex air traffic, it is necessary to conduct detailed analyses of the capacities of these airports and surrounding airspace. In this study, Monte Carlo simulation is used to determine the ultimate and practical capacities of the airport and surrounding airspace and compare them to identify any differences or limitations. The traffic mix, runway occupancy time and traffic distribution at airspace entry points are randomised variables that directly impact airport and airspace capacities and delays. The study aims to determine the current capacities of the runways and routes in the metroplex airspace and project the future capacities with the addition of new facilities. The results demonstrated that the actual bottleneck could be experienced in airspace, rather than runways, which was the focus of the previous literature. Thus, this study will provide valuable insights for stakeholders in the aviation industry to effectively manage air traffic in the metroplex airspace and meet the growing demand.

Capacity and demand forecasting in Urban Air Mobility - A simulation of air transportation in Hamburg

This paper presents an innovative approach to urban air transportation planning through the development of a "digital twin" representation encompassing airspace and infrastructure elements, including route structures, existing airports, and future vertiports. The primary objective is to formulate a model that mirrors the dynamics of contemporary and prospective urban air mobility (UAM), driven by the focus on sustainability and electrification in the transportation sector. Given the multifaceted advancements in both personal aerial vehicles (PAVs) and unmanned aerial vehicles (UAVs), along with the emergence of cutting-edge electric aircraft, a multitude of scenarios become feasible. The air transport system we focus on, is the urban area of Hamburg, Germany, and its two inner-city airports and a prototype vertiport. Anticipating a growth in demand for air transport services for the nearly 1.9 million current residents of Hamburg over the next two decades, we assume various feasible scenarios, which in turn would result in an increasing energy demand. Our SIMMOD simulation model utilizes flight schedules for each airport in Hamburg, to which flights are incrementally added until practical capacity is reached at 80% above baseline levels. Our findings reveal that, as demand increases at Hamburg Airport and average operational delay approaches the level-of-service (LoS) threshold of 4 minutes per flight, the necessity for expanded air traffic and ground infrastructure becomes evident. Electric grids should be dimensioned according to these demands. Our results suggest that the capacity of the Hamburg airspace is currently limited by the main airport, and that the Airbus production facility in Hamburg-Finkenwerder can substantially augment its capacity by constructing a parallel taxiway alongside its runway. Moreover, vertiport capacity primarily is limited by factors such as UAV parking and charging positions, drone speeds, turnaround times, available airspace, degree of autonomy and the length of service routes.

A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information

A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information

A graph multi-attention network for predicting airport delays

Predicting airport delays is of great importance for aviation operations, from the development of effective air traffic management strategies to the reallocation of airline resources. In this paper, the long-term prediction of the next 24 h of network-wide delays is investigated. The sensitivity to error propagation over long time periods as well as dynamic spatial correlations and non-linear temporal correlations of the aviation network are considered. An external impact modeling module is introduced to account for the influence of weather on flight delay patterns. A Spatial-Temporal Gated Multi-Attention Graph Network (STGMAGNet) considering external impact to predict airport delays is then developed. We validate our model on a flight delays dataset collected from the Bureau of Transportation Statistics of US, covering January 1, 2019, to December 31, 2019. In long-term (input-24-predict-24 setting) forecasting, STGMAGNet provides state-of-the-art accuracy, with a MAE reduction of at least 21% averaged in arrival delay prediction, 18% averaged MAE reduction in departure delay prediction compared to MLP, LSTM, Seq2Seq and Transformer. Our model can enable aviation management to shift from a reactive to proactive approach, thus enhancing its operational efficiency and overall performance.

Transport causality knowledge-guided GCN for propagated delay prediction in airport delay propagation networks

Flight delays pose a worldwide challenge that significantly affect the safety and efficiency of air transportation systems. However, propagated delay prediction, as well as its causality among airport delay propagation networks, has not considered some crucial issues regarding spatiotemporal dependence and propagation relationships. Thus, this study proposes a transport causality knowledge-guided extended graph convolutional network (GCN) framework to tackle these issues. In particular, a causality knowledge-guided airport delay propagation network (ADPN) is developed using the second modified transfer entropy (SMTE) principle. Furthermore, a causality-embedded adjacency matrix is utilized by an extended GCN for propagated delay prediction. Comprehensive validations and results indicate that the proposed method benefits significantly from the causality knowledge, and increases the prediction performances up to 15.51%. Thus, transport causality is significant and efficient for understanding propagated delay features and airport delay propagation network characteristics.

Airport Cluster Delay Prediction Based on TS-BiLSTM-Attention

To conduct an accurate and reliable airport delay prediction will provide an important basis for the macro control of an airspace delay situation and the dynamic allocation of airspace system capacity balance. Accordingly, a method of delay prediction for target airports based on the spatio-temporal delay variables of adjacent airports is proposed in this paper. First, by combining the complex network theory, we first extract the topology of the airport network and create airport clusters with comparable network properties. Second, we develop the TS-BiLSTM-Attention mode to predict the delay per hour for airports in the cluster. As the spatio-temporal feature variables, the arrival delay of airport cluster-associated airports and the delay time series of landing airports are utilized to reach the conclusion. The experimental results indicate that the delay prediction predicated on clusters is superior to that based on data from a single airport. This demonstrates that the delay propagation law derived from cluster data based on spatio-temporal feature extraction can generalize the delay propagation characteristics of airports within clusters.

Wireless Network-Aided Delay Information System Correlation with Airport Grid Distribution Based on Multideterminants Big Data

The intelligent sensing and communication technology in the airports’ grid information system provides a multidimensional big data set for analyzing flight delays. These data from air traffic control, weather, and multiple determinants will cause initial flight delays. Due to the influence of adjacent flight time correlation, the initial delay causes the delay of subsequent flights, discovered by mining information sensing data, forming the phenomenon of flight delay diffusion. Different determinants will lead to the delay diffusion form of different regions, and more seriously, it will lead to “disaster area” delay in the whole regional grid information structure. To analyze the spatial impact of each factor on flight delay and explore the regional distribution of delay determinants, this paper combined the spatial regression model and determined the key explanatory variables by statistical and processing of the aviation system data. The case study showed the spatial airport delay characteristics in terms of aircraft movements in China. After processing intelligent sensing and communication data, the results show that there is a spatial effect between airports in terms of delay and determinants. The high-delay clusters of delay constraints principally occurred in the Beijing-Tianjin-Hebe and Yangtze River Delta urban agglomerations. Direct flights, weather, new flight routes, take-off, and landing capacity have a more critical impact on spatial airport delays. The use of Internet of Things technology to perceive, analyze, and integrate multiple information of airport delay and combine spatial analysis models can accurately mine delay characteristics and effectively achieve digital and intelligent flight delay management.

Modelling and assessment of the arrival and departure process at the terminal area: A case study of Chennai international airport

Airport and terminal area traffic congestion is a growing concern of the air traffic management (ATM) system. A major step towards mitigating the impacts of congestion is understanding the fluctuating arrival and departure process at airports. Exploring the intrinsic time-varying nature of the arrival and departure process enhances this understanding, hence, is instrumental in formulating operational policies for managing airport capacity and delays. This paper presents a data-driven statistical framework to investigate, understand, and model the time-varying fluctuations in the aircraft arrival and departure process. We have considered one-month data obtained from Chennai international airport operations for this study. The dynamic evolution and fluctuation characteristics of arrivals and departures are conducted using Seasonality and Multifractal Detrended Fluctuation Analysis. Six theoretical probability distributions were used to fit the observed aircraft arrivals and departures at different temporal scales using the maximum likelihood estimation method. This study also develops a G1(t)/G2(t)/KFirst Come First Serve dynamic queuing model for a day’s arrival and departure process. The results suggest that arrival and departure processes exhibit weak but visible multifractal characteristics. Fluctuations are higher at a smaller temporal scale compared to a larger scale, and the probability distributions of arrival and departure are also time-varying. The framework has universal applicability, and the insights have important implications for operational policies on the air transportation workforce and infrastructure management.

An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality

The airport network is a highly dynamic and complex network connected by air routes, and it is difficult to study the impact of delays at one airport on another airport by means of human intervention. Due to the delay propagation law contained in the delay time series, some studies have used Granger causality and transfer entropy to explore whether there is a causal relationship between any two airports. However, no research has yet established a delay causal network from the perspective of the airport network as a whole. To this end, an attention mechanism is introduced into the deep convolutional network architecture, and a deep temporal convolution prediction model considering the attention mechanism is proposed, so as to establish the relationship between different airport delay time series under the same network architecture. According to the attention factor score, the delay propagation causality between airports is preliminarily screened, and the direct causality is verified based on a t-test and propagation delay analysis. Taking China’s civil airport network as an example, the method proposed in this paper can not only discover the causal relationship of delays between airports but also characterize the strength of the relationship. Further analysis found that each airport is affected by an average of six airports, and airports with small delays are more likely to be affected by other airports.

Construction and Economic Aspects of Bullet Train in Comparative Analysis with Conventional Train

As one of India's most ambitious projects, a High-Speed Rail (HSR) line between Mumbai and Ahmadabad, gains attraction, the country is poised to create history. On the Global HSR, take a major step forward in terms of development and join the league of industrialized nations. Highway gridlock, airport delays, and unpleasant journeys will soon be a thing of the past. Our very own 'bullet' train, so named because of its bullet- like shape and speed, will be tearing through the landscape of west India, traversing the 508 kilometer trip between the two financial capitals in just over two hours. When compared to existing journey times of roughly nine hours (by bus) or six hours (by train), this will save a significant amount of time (by conventional railways). The HSR project will completely transform this environment and will affect the way we Indians travel. The state-of-the-art high-speed trains, based on Japanese Shinkansen technology, will travel at 320 km/h, more than twice the speed of Indian Railways' fastest train, the Gatiman Express, which travels at 160 km/h, and we, as passengers, will get to experience one of the best HSR technologies available globally, providing the highest levels of safety, comfort, and reliability as we board this train.

Timescales of delay propagation in airport networks

Flight delays persist and spread in airport networks due to high interconnectivity in the air transportation infrastructure. How quickly delay propagates between two airports is determined by factors such as the number of flights between airports, the duration of the flight, presence of disruptions, and schedule buffers. Accurate estimation of the time for delay propagation can improve system predictability and reliability. However, noisy airport delay data, along with a lack of visibility into airline scheduling and disruption management strategies, result in a challenging estimation problem for such propagation timescales. We present an algorithm to estimate statistically significant time lags between airport delays from noisy, aggregate operational data. The algorithm uses sliding correlation windows to extract the airport pairs with stable delay lags. We apply our method to identify different timescales of interactions for US airport delays in 2017. Our analysis yields two main results: (1) The most stable lags between airport delays involve the Northeast airports; (2) The stable lags between two airports are negatively correlated to the scheduled flight times between the same two airports. These results regarding delay propagation speeds have potential implications for delay prediction models and airline schedule design.

Research on delay propagation mechanism of air traffic control system based on causal inference

In the air traffic control (ATC) system, delays are tightly coupled among airports. Understanding the interactions of these delays is a prerequisite for accurate prediction. However, most researches in this field depend on correlation methods, and few studies apply causal discovery methods. The latest approach uses the cause-and-effect diagram discovery algorithm PCMCI to mine the causal dependencies between time series. In the application context of the ATC system, not all delays have the propagation effect because of a series of optimization measures by airports and airlines. This paper proposes an extreme event measurement mechanism to solve the above problem by setting the airport thresholds adaptively through a complete search of the parameter space. The improved method allows for a more accurate exploration of the time lag delay propagation relationship between airports. The experimental simulation results show that the algorithm in this paper can extract time lag causality in nonlinear, multidimensional time series considering the ATC system characteristics. In addition, we use the causality of airport delays to construct an extreme delay propagation network (EDPN), use complex network theory and related metrics to analyze the network at the system level, and propose a new critical airport identification metric. In the case study of the U.S. aviation system, we find that small airports have a weak ability to mitigate extreme delays and are more likely to propagate delays to other airports in addition to their own impacts; Larger airports have strong mitigation and robustness to extreme delays but suffer from overload operations. These findings are helpful to explain the interactions of delay processes, and can provide specific theoretical support in improving system operation efficiency and developing delay propagation mitigation measures.

Analysis of Airport Risk Propagation in Chinese Air Transport Network

In recent years, due to the close coupling between airports, airport risk propagation has become a huge challenge. However, it has not been fully understood on the network level. Airport risk can be transferred through other airports owing to connected resources. In this study, we consider two risk factors including airport delay and saturation and propose a risk coupling model based on a clustering algorithm to fit the index and form risk series. To understand the risk propagation mechanism, we build risk propagation networks based on the Granger Causality test, and we apply complex network theory to analyze the evolution of the risk propagation network. We study the regular pattern of risk propagation from perspectives of time and space. Through network analysis, we find four time stages in the risk propagation process and the participation of airports in risk propagation has a positive correlation with airport sizes. In addition, more large airports tend to prevent risk propagation in unoccupied and normal situations, while small airports perform better than large airports in busy situations. Via the conclusion, our work can assist airlines or air traffic managers in controlling the scale of risk propagation before its key time turning point. By identifying the critical airport level and related factors in risk propagation, they can also reduce single airport risk and risk participation through corresponding risk control measures, finally avoiding the large-scale spread of risk and reducing delay or cancellation of more flights.

Evaluation and Analysis of the Impact of Airport Delays

To characterize the real dynamic process of delay propagation between airports and to understand the mechanism of delay propagation from a global perspective, this paper establishes a network of delay propagation relationships between airports based on causal analysis and conducts an example analysis. First, the flight operating status data processing is used to obtain the arrival delay time series of each airport. Then, the delay propagation relationship between airports is analyzed in pairs through the causal analysis method to obtain the delay propagation relationship network between all airports. Finally, the complex network theory and related indicators are used. The network is analyzed, and these analysis results can provide theoretical support for the formulation of delay propagation mitigation measures.

Delay propagation network in air transport systems based on refined nonlinear Granger causality

To probe deeply into the underlying mechanism of flight delay propagation, we construct the delay propagation networks among airports via refined nonlinear Granger causality, which is more fitting on nonlinear airport delay data and obtain more precise causal estimation. Specifically, the causal analysis method is used to determine the delay propagation relationship between airports. The delay propagation relationship of these airport pairs constitutes an airport delay propagation network, in which the nodes and edges of the network, respectively, represent the specific airport and the delay propagation relationship between airports. Complex network theory is then applied to analyse the global structure of delay propagation networks, indicating that small airports are inclined to cause or exacerbate delays. In addition, the airlines’ delay propagation networks display that the delay propagation by large airlines spread wider while the delay propagation by small airlines spread faster. These findings can be employed to design strategies for delay propagation dampening.