دانلود رایگان مقاله مدل سازی تاخیر احتمالی نامتقارن ورود هواپیما

abstract

The main purpose of this paper is to present an asymmetric logit probability model to estimate and predict the daily probabilities of delay in aircraft arrivals. The proposed model takes into account statistical regularity, noting that more arrivals are on time than delayed, thus reflecting an asymmetric pattern of behaviour. The data analysed were obtained from the BTS and IATA databases for December 2014, corresponding to delays within the US airspace system for each carrier, measured at various US airports. The model was evaluated by analysing both inesample and outeofesample data, for main and control samples. The performance of the proposed asymmetric Bayesian logit model was compared with that of two others: frequentist logit and symmetric Bayesian logit. The main conclusion drawn is that the model we propose obtains the best fit, according to the statistics considered, and identifies a novel delaying factor, namely distance, which is not identified by the other models analysed.

5. Summary and conclusions

This study analyses the use of an asymmetric Bayesian logit model to estimate the probability of aircraft delay, taking into account the asymmetric pattern of arrival delays at U.S. airports. To the best of our knowledge, asymmetric Bayesian logit models have not previously been applied in this setting and with these intentions. We evaluated this model by comparing its results with those obtained by the frequentist and symmetric Bayesian approaches. The main results obtained show that, according to the frequentist and standard Bayesian logit methods, the departure delay, the size of the airline, the size of the airport and the day of the flight (Tuesdays and weekends) are statistically significant factors (at the 1% significance level) to explain the probability of delay. Our study shows that arrival delay is strongly related to the originedeparture delay. The latter delay is attributed to operating procedures (i.e., the first flight segment of the day typically departs late). In our asymmetrical Bayesian model, we also identify an important new delay factor with respect to the frequentist and symmetric Bayesian models, namely the distance, in miles, between airports (statistically significant at 1%). Furthermore, the importance of incorporating asymmetry into the model is clearly corroborated by the information criteria, the percentage of correct fit and the cestatistic based on the ROC curve.