Modeling Probability Distributions of Primary Delays in the National Air Transportation System

Abstract

Flight delays impose enormous costs that are exacerbated by propagation effects. Development and evaluation of advanced schedule planning approaches for mitigating propagated delays require primary delay distribution models with a long prediction horizon of several weeks to months. This paper presents a hybrid modeling approach combining logistic regression and quantile regression. Two large data sets, each consisting of approximately 6 million flights, were used as training and testing samples to identify types of factors affecting primary delay distributions. Several insights were obtained into the factors affecting delay distributions. Coefficient estimates were found to have a high statistical significance and intuitive interpretation of their signs and relative magnitudes. Results from model estimation, validation, and testing show that fit values are stable across data sets, avoiding overfitting. The relative explanatory power of various types of predictive factors—including distance factors, seasonality factors, time-of-day factors, airport factors, and airline factors—is quantified. A new method is provided for quantifying the fit of the joint model in the form of the log likelihood of the observed data, and the model provides a considerable improvement in log likelihood.

Factors Affecting the International Flight Delays and Their Impact on Airline Operation and Management and Passenger Compensations Fees in Air Transport Industry: Case Study of a Selected Airlines in Europe

“…Liu, Zhu, and Qi (2016) [28]; Wang and Vaze (2016) [29]; Simaiakis and Balakrishnan (2016) [31]; Liang and Chaovalitwongse (2009) [27].…”

“…Others [28] propose an optimization model for flight plans to minimize delays. While some [29] suggest flight planning models with the use of logistic and quantitative regression methods. These models would predict the situation for up to several months ahead.…”

Minimizing the Congestion Index and Mode Share of Traffic Congestion in Urban Area

“…The capital city of Malaysia, Kuala Lumpur is expected to grow up to 10 million residents by 2020 [5]. In 2017, the city has reached almost thirteen million number of vehicles on the road which constitutes 98 percent of the private vehicles' trips per day [6]. The cause of heavy traffic congestion in Kuala Lumpur is due to the high dependency on private vehicle and low usage of public transportation [7].…”