Statistical characterization of airplane delays

Airport time profile construction driven by flight delay prediction

Slot structure is the equilibrium result of market demand side and slot resource supply side, while slot parameters reflect the operational support capacity of the aviation system. Time parameters reflect the operational support capability of the aviation system. Time structure should not only reflect changes in market demand, but also meet the constraints of operational efficiency. Constructing a reasonable 18-24 h timetable profile for busy airports that meets normal expectations for declared capacity and seasonal scheduling is a challenge in civil aviation slot management. This study utilizes historical data on airport flights and weather conditions to establish a regression prediction model for the time structure using K-means clustering and partial least squares regression. Additionally, ensemble learning is employed to forecast flight delay levels. The findings demonstrate that random forest yields favorable results in regression and prediction tasks, allowing for the integration of upper (good weather) and lower (severse weather) limits of the time profile with delay predictions as time parameter intervals. Consequently, the flights falling within these intervals achieve an average delay level of less than 15 min which meets the expectations of normal flight.

Neural complexity through a nonextensive statistical-mechanical approach of human electroencephalograms

The brain is a complex system whose understanding enables potentially deeper approaches to mental phenomena. Dynamics of wide classes of complex systems have been satisfactorily described within q-statistics, a current generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human electroencephalograms of typical human adults (EEG), very specifically their inter-occurrence times across an arbitrarily chosen threshold of the signal (observed, for instance, at the midparietal location in scalp). The distributions of these inter-occurrence times differ from those usually emerging within BG statistical mechanics. They are instead well approached within the q-statistical theory, based on non-additive entropies characterized by the index q. The present method points towards a suitable tool for quantitatively accessing brain complexity, thus potentially opening useful studies of the properties of both typical and altered brain physiology.

Characteristics of flight delays during solar flares

Solar flares are one of the severest solar activities that have important effects on near-Earth space. Previous studies have shown that flight arrival delays increase as a result of solar flares, but the intrinsic mechanism behind this relationship is still unknown. In this study, we conducted a comprehensive analysis of flight departure delays during 57 solar X-ray events by using a huge amount of flight data (~ 5 × 10⁶ records) gathered over a 5-year period. It is found that the average flight departure delay time during solar X-ray events increased by 20.68% (7.67 min) compared to quiet periods. Our analysis also revealed apparent time and latitude dependencies, with flight delays being more serious on the dayside than on the nightside and longer (shorter) delays tending to occur in lower (higher) latitude airports during solar X-ray events. Furthermore, our results suggest that the intensity of solar flares (soft X-ray flux) and the Solar Zenith Angle directly modulate flight departure delay time and delay rate. These results indicate that communication interferences caused by solar flares directly affect flight departure delays. This work expands our conventional understanding of the impacts of solar flares on human society and provides new insights for preventing or coping with flight delays.

Additional flight delays and magnetospheric-ionospheric disturbances during solar storms

Although the sun is really far away from us, some solar activities could still influence the performance and reliability of space-borne and ground-based technological systems on Earth. Those time-varying conditions in space caused by the sun are also called solar storm or space weather. It is known that aviation activities can be affected during solar storms, but the exact effects of space weather on aviation are still unclear. Especially how the flight delays, the top topic concerned by most people, will be affected by space weather has never been thoroughly researched. By analyzing huge amount of flight data (~ 4 × 10⁶ records), for the first time, we quantitatively investigate the flight delays during space weather events. It is found that compared to the quiet periods, the average arrival delay time and 30-min delay rate during space weather events are significantly increased by 81.34% and 21.45% respectively. The evident negative correlation between the yearly flight regularity rate and the yearly mean total sunspot number during 22 years also confirms such correlation. Further studies show that the flight delay time and delay rate will monotonically increase with the geomagnetic field fluctuations and ionospheric disturbances. These results indicate that the interferences in communication and navigation during space weather events may be the most probable reason accounting for the increased flight delays. The above analyses expand the traditional field of space weather research and could also provide us with brand new views for improving the flight delay predications.