• Journal of Industrial Technology
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• v.20 no.A
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• pp.159-167
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• 2000

In the study rainfall frequency analysis attemped the many specific property data record duration it is differance from occur to error-term and probability ditribution of concern manifest. error-term analysis of method are fact sample data using method in other hand it is not appear to be fault that sample data of number to be small random variates. Therefore, day-rainfall data: to randomicity consider of this study sample data to the Monte Carlo method by randomize after data recode duration of form was choice method which compared an assumed maternal distribution from splitting frequency analysis consequence. In the conclusion, frequency analysis of chuncheon region rainfall appeared samll RMSE to the Gamma II distribution. In the rainfall frequency analysis estimate RMSE using random variates great transform, RMSE is appear that return period increasing little by little RMSE incresed and data number incresing to RMSE decreseing.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.434-440
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• 2022

The ICAO (International civil aviation organization)recommended the implementation of the GANP (global air navigation plan) for strategic decision-making and air traffic management evaluation. In this study, we proposed a new method for finding the route distance from KPI (key performance indicator) 05 actual route extension presented for air traffic management evaluation. For this purpose, we collected trajectory data for one month and calculated the en-route distances using the methods presented in ICAO and the methods presented by this author. In the ICAO method, the intersection point must be estimated through the equation of a circle for radius 40 NM and the equation of a straight line for an inner and outer point close to a circle in the track data, and four flight distances are calculated to calculate the en-route distance. In the method presented in this study, two flight distances are calculated without estimating the intersection point to calculate the en-route distance. To determine the error between the two methods, we used the performance evaluation index RMSE (root mean square error) and the determination factor R² of the regression model.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.135-142
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• 2010

The convenient techniques for predicting the bus arrival time have used the data obtained from the buses belong to the same company only. Consequently, the conventional techniques have often failed to predict the bus arrival time at the downstream bus stops due to the lack of the data during congestion time period. The primary objective of this study is to overcome the weakness of the conventional techniques. The estimation model developed based on the data obtained from Bus Information System(BIS) and Bus management System(BMS). The proposed model predicts the bus arrival time at bus stops by using the data of all buses travelling same roadway section during the same time period. In the tests, the proposed model had a good accuracy of predicting the bus arrival time at the bus stops in terms of statistical measurements (e.g., root mean square error). Overall, the empirical results were very encouraging: the model maintains a prediction job during the morning and evening peak periods and delivers excellent results for the severely congested roadways that are of the most practical interest.