Airport and route classification by modelling flight delay propagation

Flight delay can be divided into the root and propagated delays for identification and analysis of airports/routes that have a great influence on actual flight delay using flight operations raw data (tower logs) for South Korean domestic flights. In addition, the presented concept of “generated delay” classifies airports as affecting other airport delays or affected by other airport delays. The generated delay refers to the delay time at a particular airport/route, and this delay thereafter actually propagates to other airports/routes. In this study, the generated and propagated delays were displayed on a two-dimensional graph, and the airports/routes were grouped according to delay characteristics. Group A represented airports with high generated and propagated delays, and group B represented airports that are highly affected by delays of preceding airports/routes. Group C represents airports where newly formed delays affect other airport delays, but these airports mitigate delays from other airports. Airports in group D have relatively low delays and propagation. Thus, we targeted airports belonging to groups A and C because of their delay propagation impact on other routes/airports, which must be reduced by decreasing the root delay from targeted routes/airports. Among the airports, Jeju international airport (CJU) had the highest average delay time and propagated flight delay time with similar averaged generated delay times. Among the routes, departure flights from various airports to CJU had significant propagation effects on the subsequent flights. CJU and related routes have a very large impact on domestic flight delays because South Korean domestic airline routes are concentrated on CJU. However, there has been no quantitative analysis, and it is meaningful that the quantitative analysis results were presented in this study. In addition, we suggest that other airports such as GMP (Gimpo), CJJ (Cheongju), WJU (Wonju), and KUV (Gunsan) have a significant impact on domestic flight delays.     

Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

To mitigate air traffic demand-capacity imbalances, large European airports implement strategic flight schedules, where flights are assigned arrival/departure slots several months prior to execution. We propose a generic assessment of such strategic schedules using predictions about arrival/departure flight delays and cancellations. We demonstrate our approach for strategic flight schedules in the period 2013–2018 at London Heathrow Airport. Together with the development of dedicated strategic flight schedule optimization models, our proposed approach supports an integrated strategic flight schedule assessment, where schedules are evaluated with respect to flight delays and cancellations.     

Endogenous control of service rates in stochastic and dynamic queuing models of airport congestion

Airport congestion mitigation requires reliable delay estimates. This paper presents an integrated model of airport congestion that combines a tactical model of capacity utilization into a strategic queuing model. The model quantifies the relationships between flight schedules, airport capacity and flight delays, while accounting for the way arrival and departure service rates can be controlled over the day to maximize operating efficiency. We show that the model estimates the average and variability of the delays observed at New York’s airports relatively well. Results suggest that delays can be extremely sensitive to even small changes in flight schedules or airport capacity.     

An Exploratory analysis of flight delay propagation in China

Delay propagation is the flight departure delay caused by the arrival delay of pre-segment flight. Chinese airline market has suffered very poor on-time performance (OTP) in recent years. It is, however, unclear whether delay propagation prevails as one major source for such problem. This study first aims to empirically quantify delay propagation in the Chinese airline market. Specifically, we shed light on heterogenous levels of delay propagations across different airports and airlines. Then, the distinct delay propagation patterns in China are also discussed and compared with other developed airline markets (e.g., the US and Europe). Our estimation is based on OTP data for over 12 million Chinese flights covering the 2015–2017 period. Specifically, it is found that 10 min arrival delay of pre-segment flight within 1 hr before the departure lead to an average of 7.49 mins delay propagation for subsequent departure flight. Arrival delay of earlier pre-segments (1–2 and 2–3 hr before the departure) leads to much less delay propagation, due to longer ground buffer. Chinese airlines arrange longer ground and flight buffer than that of the US airlines to prevent the delay propagation from accumulating along the subsequent flights in a day. Thus, unlike the US market, delay propagation is not the major reason for poor OTP in China. In addition, delay propagation is less prevailing at the Chinese hub airport. This is because China has relied on point-to-point network, which does not require sophisticated schedule coordination. And the local passengers at these Chinese hub airports have higher time value such that the Chinese airlines also try to improve OTP at these hub airports to better serve these lucrative but time-sensitive local passengers. Unlike the European LCCs, Spring Airlines, the largest low-cost carrier (LCC) in China, outperforms major full-service carriers (FSCs) in controlling delay propagation. This finding may also apply to other Northeast Asian LCCs sharing common operational characteristics as Spring Airlines. Last, we find that airlines purposely tolerate moderate departure delays of up to 15 min, which is the threshold that defines delays, no matter whether the pre-segment flight arrives late or on-time. The relevant policy and managerial implications are also discussed.     

Airport curfew and scheduling differentiation: Domestic versus international competition

This study investigates the strategic flight departure scheduling on domestic and short-haul international routes from regional airports in Korea. Airport-level data were constructed for non-stop routes from 2010 to 2018. This study examines the effects of changes in scheduling constraints induced by airport night curfews on domestic and international routes through competition intensity. The empirical findings suggest that competition leads to less differentiated flight departure times on domestic routes from the two regional airports. However, competition leads to more differentiated flight departure times on international routes from Daegu airport, which has a new night curfew, while a clustered departure pattern is found for international routes from Cheongju airport. An obvious pattern of differentiated departure times is found after the easing of night curfews in 2014, along with the expansion in LCCs.     

Validating delay constructs: An application of confirmatory factor analysis

This paper proposes to use confirmatory factor analysis (CFA) to evaluate the relationship between six observed variables (arrival and departure counts, arrival and departure demand, taxi-out and airborne delays) and their underlying latent (unobserved) constructs (operations, demand, and delays) at six of the most delayed airports (EWR, JFK, LGA, MIA, ORD, and SFO) during the calendar years of 2006–2008. The CFA revealed a good fit between the six observed variables and the three factors that may explain on-time performance except in the case of JFK. The use of CFA can help analysts validate constructs when theory supports a priori predictions and relationships between observed and unobserved variables.     

An analysis of Brazilian flight delays based on frequent patterns

In this paper we applied data indexing techniques combined with association rules to unveil hidden patterns of flight delays. Considering Brazilian flight data and guided by six research questions related to causes, moments, differences, and relationships between airports and airlines, we evaluated and quantified all attributes that may lead to delays, showing not only the main patterns, but also their chances of occurrence in the entire network, in each airport and airline. We observed that Brazilian flight system has difficulties to recover from previous delays and when operating under adverse meteorological conditions, delays occurrences may increase up to 216%.     

Using causal machine learning for predicting the risk of flight delays in air transportation

Delays in air transportation are a major concern that has negative impacts on the airline industry and the economy. Given the complexity of the National Air Space system, predicting the risk of flight delays and identifying significant predictors is vital to risk mitigation. The purpose of this paper is to perform data mining using causal machine learning algorithms in the USELEI process (Understanding, Sampling, Exploring, Learning, Evaluating, and Inferring) to predict the probability of flight delays in air transportation using data collected from different sources. The findings indicated significant effects of predictors, including reported arrivals and departures, arrival and departure demands, capacity, efficiency, and traffic volume at the origin and destination airports on the risk of flight delays. More importantly, causal interrelationships among variables in a fully structural network are presented to how these predictors interact with one another and how these interactions lead to delay incidents. Finally, sensitivity analysis and causal inference can be performed to evaluate various what-if scenarios and form effective strategies to mitigate the risk of delays.     

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

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

Efficiency measurement of Chinese airports with flight delays by directional distance function

This paper employs directional distance function to evaluate the technical efficiency of twenty major Chinese airports from 2006 to 2009 within a joint production framework of desirable and undesirable output (i.e. flight delays). The results indicate that the overall average efficiencies of Chinese airports increased over the period of time. The international hub airports are found to operate at higher efficiency level, which indicates that these airports run well in producing more desirable outputs and controlling flight delays. Although the average efficiency score of listed airports is higher than non-listed ones, the difference between the two group is statistically insignificant. A comparison between the results without and with flight delays show that several airports experienced significant changes in their efficiency scores after considering undesirable output.     

A border effect in airport choice: Evidence from Western Europe

Some commentators suggest that air travelers are reluctant to use a foreign airport as a point of departure for their journey. Such reluctance has implications for marketing and policy measures, especially in border regions and in smaller countries, such as the Netherlands and Belgium. In this study, we tested this supposed barrier effect of national borders with empirical data.Our research is based on a subsample (n = 4083) of a large-scale survey among German, Dutch and Belgian adults. Participants were asked about their most recent flight, preferences regarding long-distance travel, and general travel behavior. The survey information about the most recent trip was linked to the Official Airline Guide with data from 18 airports in the Western European region. On average, however, only 10 airports offered a direct connection to the destination of the respondents’ most recent flight. A conditional logit model with varying choice sets allowed us to model only the relevant airports per respondent, while accounting for other known determinants in airport choice, such as travel time, airline competition, and number of available flights.The model results confirmed our expectations: people strongly prefer a departure airport situated in their own country. The model predictions suggest that without the border effect the share of people in our sample departing abroad would rise from 7.7% to 18.7%. Experience (with multiple airports as a point of departure) mitigates the barrier effect of the border. The performance of our model is good: the median probabilitie for the chosen alternative is 68%. Our research stresses the importance of the barrier effect of borders in airport choice: even when traveling across borders in the European sky, national borders remain important.     

The impact of airport capacity constraints on future growth in the US air transportation system

This paper simulates airline strategic decision making and its impact on passenger demand, flight delays and aircraft emissions. Passenger flows, aircraft operations, flight delays and aircraft emissions are simulated for 22 airports in the US, under three airport capacity scenarios. The simulation results indicate that most system-wide implications for operations and environmental impact seem to be manageable, but local impacts at congested hub airports may be significant. The response of the air transportation system to avoid airports with high delays could significantly impact passenger demand and air traffic for these and directly dependent airports. The simulations also suggest that frequency competition effects could maintain flight frequencies at high levels, preventing a significant shift toward larger aircraft, which would otherwise reduce the impact of the capacity constraints.     

Cascading failures in airport networks

Cascading failure phenomena can appear in complex networks that distribute flows of information, people or goods, when flow going through nodes or edges exceeds the capacity of network nodes or edges. Cascading failure models from previous research are not adequate for airport networks, as flow is not continuous, and load has to be redistributed among close airports, rather than previously existing connections. With these constraints in mind, we have defined an algorithm to simulate the management of cascading failures in airport networks. We use the algorithm to evaluate the effectiveness of several selection rules of alternative departure and arrival airports to affected flights to reduce the impact of the cascading failure. We have applied the algorithm to the Oceanic Airport Network to assess the impact of several incidents. Results show that selection rules of arriving airports have significant impact in reducing the effect of incidents affecting central airports.     

Aircraft routing for on-demand air transportation with service upgrade and maintenance events: Compact model and case study

This paper addresses a problem faced by airline companies that offer on-demand flight services. Given a list of flight requests, the company has to assign its aircraft to these requests while minimizing operational costs. The main issue in this planning process involves the positioning of aircraft when they are not available at the airports of customer departure. The cost of this positioning should be as low as possible, as the customers’ expenditures are proportional to the requested flight hours only. We propose a compact optimization model to support decision making in this situation. It takes into account the mandatory aircraft maintenance events and the possibility of flight upgrades according to the their impact on the operational costs. One important and novel feature of this model is that it allows the anticipation or postponement of the beginning of flights and maintenance events within a given tolerance, giving more freedom to the decision making process. This research is motivated by a case study carried out with a fractional management airline company that operates in European and Asian countries. Computational experiments using real-life data collected from the company show that the proposed model can be solved quickly using general-purpose optimization software, including open-source alternatives. The results indicate that the obtained solutions lead to significant reductions in the operational costs and hence can be used in practice for effective decision making.     

Has market concentration fostered on-time performance? A case study of seventy-two U.S. airports

The study compares a multivariate with a quantile regression model to determine whether utilized airport capacity, departure and airborne delays, departure and arrival demand, and market structure explained variations in on-time gate arrivals and arrival delays. In both models, airport departure delays, arrival and departure demand explained variations in the two response variables in prioritized and non-prioritized metroplexes, holding other variables constant. After 2008, the consolidation of the airline industry through mergers coincided with the implementation of NextGen programs, which may have contributed to improvements in on-time performance, especially at prioritized metroplexes where airspace was redesigned.     

Airport traffic complexity and environment efficiency metrics for evaluation of ATM measures

For a given (current or planned) traffic demand, different air traffic management measures could result in different airport traffic complexity and efficiency. This paper presents the research on the relationship between airport traffic complexity and time and environmental efficiency for different air traffic control (ATC) tactics applied to the given or planned airport layout. Emphasis is placed on the evaluation of airport traffic complexity, aircraft fuel consumption, gas emissions and time efficiency for different ATC tactics and/or airport airfield layouts. For busy airports during peak hours, arrival queuing delays, taxi-in, taxi-out times and departure queuing delays increase, which induces additional unnecessary fuel consumption, gas emission and time inefficiency. In order to find a tool which could indicate potential delay generators, a measure of airport traffic complexity – called Dynamic Complexity is proposed. Experiments were performed for airports with different airfield layouts, for different traffic demands and ATC applied tactics using SIMMOD simulation model. Traffic situations were analyzed and delays were measured. The values of airport traffic complexity, fuel consumption and gas emissions were also determined. A comparative analysis of the results show: first, the proposed airport traffic complexity metric quite satisfactorily reflects the influence of traffic characteristics upon the environmental state of the system, and second, different ATM strategic and tactical measures (airport airfield infrastructure development and applied ATC tactics) could significantly reduce traffic complexity and increase time and environmental efficiency at the airport.     

Entry effect of low-cost carriers on airport-pairs demand model using market concentration approach

The trend of open sky policies and growth of low-cost airlines, the topic of airport-pairs demand is gradually being addressed in the golden aviation circle of Northeast Asia. The variety of flight services among the four major metropolises with dual-airport systems leads to a competition-cooperation relationship existing between various airports and airlines. Therefore, this study investigates the causal relationship between the route-level passenger demand and influential factors using aggregate data collected through website observations. The empirical study focuses on direct flights of airport-pair routes among Taipei, Shanghai, Seoul, and Tokyo. Results of the passenger regression model indicate that frequency, code-share, and morning flights have positive impacts on increasing passenger numbers for airlines. Further, the market concentration degree of Herfindahl-Hirschman Index and entry effect of low-cost carriers are important for the route-level passenger demand. In addition, routes with departures and arrivals in hub airports have a considerable attraction relative to other airport-pair routes. Finally, the proposed passenger model performs well in predicting market share, especially for routes with high demand.     

Network-wide assessment of ATM mechanisms using an agent-based model

This paper presents results from the SESAR ER3 Domino project. Three mechanisms are assessed at the ECAC-wide level: 4D trajectory adjustments (a combination of actively waiting for connecting passengers and dynamic cost indexing), flight prioritisation (enabling ATFM slot swapping at arrival regulations), and flight arrival coordination (where flights are sequenced in extended arrival managers based on an advanced cost-driven optimisation). Classical and new metrics, designed to capture network effects, are used to analyse the results of a micro-level agent-based model. A scenario with congestion at three hubs is used to assess the 4D trajectory adjustment and the flight prioritisation mechanisms. Two different scopes for the extended arrival manager are modelled to analyse the impact of the flight arrival coordination mechanism. Results show that the 4D trajectory adjustments mechanism succeeds in reducing costs and delays for connecting passengers. A trade-off between the interests of the airlines in reducing costs and those of non-connecting passengers emerges, although passengers benefit overall from the mechanism. Flight prioritisation is found to have no significant effects at the network level, as it is applied to a small number of flights. Advanced flight arrival coordination, as implemented, increases delays and costs in the system. The arrival manager optimises the arrival sequence of all flights within its scope but does not consider flight uncertainties, thus leading to sub-optimal actions.     

Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system

Recent success of bicycle-sharing systems (BSS) have led to their growth around the world. Not surprisingly, there is increased research towards better understanding of the contributing factors for BSS demand. However, these research efforts have neglected to adequately consider spatial and temporal interaction of BSS station's demand (arrivals and departures). It is possible that bicycle arrival and departure rates of one BSS station are potentially inter connected with bicycle flow rates for neighboring stations. It is also plausible that the arrival and departure rates at one time period are influenced by the arrival and departure rates of earlier time periods for that station and neighboring stations. Neglecting the presence of such effects, when they are actually present will result in biased model estimates. The major objective of this study is to accommodate for spatial and temporal effects (observed and unobserved) for modelling bicycle demand employing data from New York City's bicycle-sharing system (CitiBike). Towards this end, spatial error and spatial lag models that accommodate for the influence of spatial and temporal interactions are estimated. The exogenous variables for these models are drawn from BSS infrastructure, transportation network infrastructure, land use, point of interests, and meteorological and temporal attributes. The results provide strong evidence for the presence of spatial and temporal dependency for BSS station's arrival and departure rates. A hold out sample validation exercise further emphasizes the improved accuracy of the models with spatial and temporal interactions.     

空铁联运协同下的跨域航空服务网络优化

为解决各机场竞相开设航班所导致的空域航班密度过大及与之相伴的航班延误问题,从需求管理的角度研究空铁联运网络优化及空铁协调下的客运组织问题。在建立由航班、高速铁路和空铁换乘系统构成的复合网络的基础上,以城市圈为区域单元,构建考虑公平性约束和高速铁路对航班喂给条件的上层空铁联运跨域航空服务网络优化模型和下层乘客路径选择模型的双层优化模型,旨在最小化区域间的出行总时间。在实证分析部分,对研究区域进行计算结果分析,并分析公平性指数对区域间运输系统效率的敏感性,为跨域航空服务网络优化研究提供参考依据。