Research On Airport Capacity And Delay Assessment Affected By The Weather

Severe weather has been identified as the most important causal factor for causing airportcapacity reduction. Because the weather is complicated and changeable, the impacts of weather oncapacity have significant randomness, real-time and the dynamic characteristic. In view of this, themodels in majority research were built not including the weather influence factors. Therefore, airportcapacity assessment effected by weather is the urgent issues to be solved. With the steady rise indemand for air transportation, capacity and demand imbalances caused by severe weather becomemore prominent as the most important causal factor for flight delays. Therefore, to guide flow controldecisions during the day of operations, and for post operations analysis, it is useful to establish amodel that characterizes the relation between weather and delays and create a baseline for delayestimation, so as to assess delays under similar weather and traffic demand influences.In this dissertation, the history development and the latest research results of airport capacity anddelay assessment issues were summarized, and the key problems to be solved were discussed. Theconcepts of airport capacity and delay assessment affected by weather were presented, and theevaluation process of them was analyzed respectively. The relation between weather and capacity wasdiscussed, and airport capacity assessments affected by weather were researched for weather seasonalcharacter, weather types and weather forecasts respectively. For the relation between weather anddelay，the delay model were developed, and airport delay assessment affected by weather wereresearched for discrete and continuous baseline respectively.The concepts of airport weather, delay, demand and capacity were analyzed, and the relationbetween of them were discussed. For the specific airport, the impact of weather on capacity and delaywas analyzed, and numerical example was presented.For capacity assessment affected by weather seasonal changes, season division model based timeseries was built。Capacity probabilistic distribution of historical weather was acquired to characterizeweather features, and a generic algorithm was designed to recognize several months of similarweather characteristics as a season. On this basis, according to the capacity distribution modelaffected by weather, capacity probabilistic distribution of historical weather in each season wasacquired. Finally, numerical simulation and analysis were carried out for the pacific airport, and theresults indicate that the seasons based on the capacity probabilistic distribution are in accordance withthe actual seasonal weather effects. For capacity assessment affected by weather types, the influence factors of severe weather areanalyzed, and the methodology of weather type identification with combination of decision tree andneural network was proposed, which used in the automatic identification of weather type to whicheach severe weather event belongs. On this basis, according to the capacity distribution modelaffected by weather, capacity probabilistic distribution of each weather type was acquired. Finally,numerical simulation and analysis were carried out for the pacific airport.For capacity forecasts based on weather forecasts，aiming at real-time and dynamic characteristicsof weather forecasts, the forecasts model of capacity distribution based on weather forecasts was built.Considering that capacity forecasts occur in the context of a variety of weather types, we translateprobabilistic weather forecasts into probabilistic capacity forecasts using full probability formula,which require weather forecast data and capacity probabilistic distribution of each weather type asinputs. Finally, numerical simulation and analysis were carried out for predicting capacityprobabilistic distribution on a particular day, according to weather forecast on the day.For delay assessment affected by weather based on discrete baseline, typical delay pattern modescorresponding typical weather pattern modes were studied through development of a delay estimationmodel using instantaneous queuing model, and discrete baselines was created to measure theoperational delay affected by weather. Finally, according to the weather conditions in a day, the trendof delay distribution affected by weather on the day was analyzed.For delay assessment affected by weather based on continuous baseline, a single fuzzy linearregression model was built for the linear relationship between traffic demand and weather. Taking intoaccount that the nonlinear relationship also exists between traffic demand and weather, a piece-wisefuzzy linear regression model was built. The model was solved for delay estimation, and a continuousbaselines was created to measure the operational delay affected by weather. Finally, according to theweather conditions in a day, the average delay for the day affected by weather was analyzed.Probabilistic traffic flow management considering weather is becoming a research hotspot. Thedissertation is a deep research on capacity and delay assessment affected by weather. The proposedrelated models and methods have strong pertinence and are easy to be realized. The research of thispaper is conducive to improve our country’s study level in this field, and provide the theorypreparation for the further research and application of probabilistic traffic flow management.