The “ Methodology” section will outline the discovery of correlation between flight data and weather data, and the development of on-time flight prediction. The following sections of the paper are organized as follows: the “ Literature review” section will describe related works of flight prediction. Furthermore, a tool is developed to evaluate the feasibility of the model. The model is evaluated by flight data and weather data from Januto January 31, 2019. As a result, a predictive model of on-time arrival flight is developed by using the flight data and the weather data. The relation between those pinpoint data and the pressure pattern can be proved. By using those data resources, the feature of flight performance and pressure patterns of weather is analyzed, and the sea-level pressures of 3 weather observation spots, which are Wakkanai as the most northern spot, Minami-Torishima as the most eastern spot, and Yonagunijima as the most western spot, are selected as pinpoint of weather data in order to extract the features of weather related to pressure pattern as this method of selecting pinpoint data is useful in big data analytics to discover the correlation among data. FLIGHTSTATS also offers on-time arrival performance of flight which does not reach 15 min delay. Flight data set of FLIGHTSTATS and weather data of Japan Meteorological Agency from Mato Decemare gathered. The key research in this paper is to discover the correlation between flight data and weather data. So, on-time flight is focused on and on-time flight prediction is developed because the flight performance is not affected by the company circumstances and the inevitability. As the inevitability, there is the natural disaster including a heavy snow and the typhoon. As the company circumstances, the airline is responsible for machine parts trouble and the system failure. Another is the factor of the inevitability.

One is the factor of company circumstances. There are two reasons why a flight may be delayed or canceled. The causes of cancel are 1.16% for weather, 0.04% for a mechanical reason, 0.06% for the late arrival of the aircraft to the point of departure, and 0.07% for other. As for the canceled flight, It operates the canceled flight at 1.32%. The causes of delay are 1.29% for weather, 0.62% for a mechanical reason, 14.99% for the late arrival of the aircraft to the point of departure, and 3.94% for other. The delayed flight, which is arrived on more than 15 min from arrival estimated time, is operated at 20.83%. Peach Aviation is LCC (low-cost carrier) of commercial passenger jet airliners for short and medium distance and uses AIRBUS A320-200 type machine. The cancellation rate is the percentage of canceled flights relative to the number of scheduled flights, which is the flight scheduled to be operated on the day and is different from the suspension of canceling the flight in advance. So, the delayed rate refers to flights departing later than 15 min of the scheduled departure time. Flight cancellations are not reflected in on-time performance rate. In other words, it’s the time the aircraft started to move. The departure time means the time of the block out. On-time performance rate refers to flights which are operated and departing within 15 min of the scheduled departure time. Ministry of Land, Infrastructure, Transport and Tourism reported Japanese airliners’ performance on 2017.

Furthermore, feasibility of the predictive model is evaluated by developing a tool of on-time arrival flight prediction. As a result, on-time arrival fight is predicted at 77% of the accuracy with using Random Forest Classifier of machine learning. The relation between pressure pattern and flight data of Peach Aviation, which is LCC (low-cost carrier) in Japan, are clarified, and it is found that the sea-level pressures of 3 weather observation spots, which are Wakkanai as the most northern spot, Minami-Torishima as the most eastern spot, and Yonagunijima as the most western spot, can classify the pressure patterns. A predictive model of on-time arrival flight is proposed with using flight data and weather data. Due to bad weather, a mechanical reason, and the late arrival of the aircraft to the point of departure, flights delay and lead to customer dissatisfaction. An important business of airlines is to get customer satisfaction.

Ontime flight statistics