Study of Flight Departure Delay and Causal Factor Using Spatial Analysis

Abstract

Analysis of flight delay and causal factors is crucial in maintaining airspace efficiency and safety. However, delay samples are not independent since they always show a certain aggregation pattern. Therefore, this study develops a novel spatial analysis approach to explore the delay and causal factors which is able to take dependence and the possible problem involved including error correlation and variable lag effect of causal factors on delay into account. The study first explores the delay aggregation pattern by measuring and quantifying the spatial dependence of delay. The spatial error model (SEM) and spatial lag model (SLM) are then established to solve the error correlation and the variable lag effect, respectively. Results show that the SEM and SLM achieve better fit than ordinary least square (OLS) regression, which indicates the effectiveness of considering dependence by employing spatial analysis. Moreover, the outcomes suggest that, aside from the well-known weather and flow control factors, delay-reduction strategies also need to pay more attention to reducing the impact of delay at the previous airport.

Analysis of the Foggy Days and Its Key Mechanisms Over the Yangon Flight Region

“…To maintain airspace effectiveness and safety, it is essential to look at flight delays and their causes [21]. Weather conditions can create major delays in air travel, resulting in the cancellation or postponement of hundreds or thousands of flights, affecting the itineraries and budgets of millions of people [22].…”

Prediction of air traffic delays: An agent-based model introducing refined parameter estimation methods

“…The agent i on "1-turnaround" would update to"2-prepared" in Eq (2) if it had experienced enough time for its turnaround at t. Eq (3) denotes that the agent i on "1-turnaround" would update to "2-prepared" at t for the case that it had experienced enough time due to the carrier and the GDP reason. Eq (4) denotes that the agent i on "3-waiting" would update to "4-flying" without the capacity restrictions of the origin airport agent at t. Eq (5) denotes that the agent i on "4-flying" would update to "5-arrived" if it had experienced block time being airborne and on the ground at t. In contrast, the second item of the above Eqs (1)- (5) indicates that the state of the flight agent i failed to change at t since the conditions were not met. In particular, Eq 6 means that the state was not changed after the flight arrived.…”

“…Consequently, it is difficult to apply an analytical method to model the dynamics of system-wide delays. There are also a large number of factors that result in flight delays [ 5 ]. The demand and the capacity unbalancing of airspaces or airports originally caused most of the flight delays [ 6 ], which reduced the system robustness, resulting in flight delay propagation and even accumulation [ 7 ].…”

Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model

“…Airports issues accounted for all delays over the past year mainly due to airport weather, airport capacity, maintenance or aircraft problems, air traffic capacity, control, and limitation [ 2 ]. In the research [ 3 ], the spatial analysis has been used to find the factors which influence the departure flight delay. The research results show that, in many cases, weather is one of the most important factors, so the weather-induced prediction methods are proposed in [ 4 ], also a lot depends on the size and activity of the analyzed airport.…”