Improving Aviation Incidents using Association Rule Mining Algorithm and Time Series Analysis

Abstract

Abstract Advance technology helps to forecast and show the different hazards associated in the world of transportation systems. Despite the rapid advancement of technology, there are still huge number of aviation accidents and incidents happened. This paper concerns application of various data analytics algorithm to derive a time series forecasting method and frequent accident and incident pattern specifically for Philippine aviation setting for predicting conditions that would increase the likelihood of aviation accidents and incidents involving fatal and non-fatal activities. Data Analytics algorithms are Association Rule Mining using FP-Growth algorithms and Time Series Forecasting methods using Linear Regression, Gaussian Processes, Multilayer Perceptron, and SMOreg are applied to datasets derived from original data obtained from Department of Transportation (DOTr) with its attached agency, Civil Aviation Authority of the Philippines (CAAP) and both incident and accident records stored in “Philippine Aviation Incident Reporting System (PAIRS)” from 2008-2017. The aviation accident data is based on flight information such as aircraft attributes, aviation accident factors, type of occurrences, geographical location, weather conditions and phases of operation. The results lead to prescriptive analytics for development of business rules and help identify pattern that can define aircraft accidents/incidents occurrences. The ability to predict accidents has the main objective of saving lives and will be impactful in cost saving in terms of any aircraft damages. The result created important points in improving the aviation operation and assists in better decision-making to help create policy and future improvement for aviation-safety in the country.