Abstract
One of the top long-term threats to airport resilience is extreme climate-induced conditions, which negatively affect the airport and flight operations. Recent examples, including hurricanes, storms, extreme temperatures (cold/hot), and heavy rains, have damaged airport facilities, interrupted air traffic, and caused higher operational costs. With the development of civil aviation and the pre-COVID-19 surging demand for flights, the passengers’ complaints of flight delay increased, according to FoxBusiness. This study aims to discover the weather factors affecting flight punctuality and determine a high-dimensional scale of consequences stemming from weather conditions and flight operational aspects. Machine learning has been developed in correlation with the weather and statistical data for operations at Birmingham Airport as a case study. The cross-correlated datasets have been kindly provided by Birmingham Airport and the Meteorological Office. The scope and emphasis of this study is placed on the machine learning application to practical flight punctuality prediction in relation to climate conditions. Random forest, artificial neural network, support vector machine, and linear regression are used to develop predictive models. Grid-search and cross-validation are used to select the best parameters. The model can grasp the trend of flight punctuality rates well where R2 is 0.80 and the root mean square error (RMSE) is less than 15% using the model developed by random forest technique. The insights derived from this study will help Airport Authorities and the Insurance industry in predicting the scale of consequences in order to promptly enact and enable adaptative airport climate resilience plans, including air traffic rescheduling, financial resilience to climate variances and extreme weather conditions.
Highlights
The transportation sector is a critical part of the infrastructure that brings convenience to people and significant economic benefits to society
Lee et al [17] have tested and compared five machine learning techniques, and the results showed that the techniques with the best prediction performances were the linear regression and random forest methods, the prediction accuracy for the taxi time of each flight was not satisfactory
This section analyses the potential relationship between the flight punctuality rate at Birmingham Airport and the hypothetical variables
Summary
The transportation sector is a critical part of the infrastructure that brings convenience to people and significant economic benefits to society. The sustainability of the transportation system plays an important role in reducing energy use and air emissions [1]. With the development of society and travel demands, more travellers are choosing flights as their preferred mode of international transport due to travel time, convenience, or cost. According to the Civil Aviation Authority [2], comparing 2013 and 2017, the passenger numbers increased from 9.1 million to 13 million at Birmingham Airport in the United Kingdom, with the number of complaints from customers increasing as well. Improving the reliability of flight services would save customers time and improve customer satisfaction, but would lead to sustainable development and resource conservation
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