Assessment of airport arrival congestion and delay: Prediction and reliability

摘要

Air traffic networks are highly dependent on airport arrival processes, which are common triggers for capacity constraints and delay propagation. Arrival Manager tools aim to improve arrival flows at airports. To do so they need reliable, accurate assessments of potential congestion and delay issues. This paper sets out a methodology for predicting and evaluating the operational state of the airport arrival system. This methodology is structured in two steps: the prediction stage and the reliability stage. The prediction model is based on a Bayesian Network approach, which reflects the stochastic and time-varying nature of airport operations. It also provides insights into the interdependencies between factors contributing to airport performance. The reliability model uses a Multi-State System structure, as the airport arrival system has a large number of performance levels. It is developed via a Markov process technique. By combining these prediction and reliability models we can assess the characteristics of the airport arrival system: stationary state, availability, performance and degradation. The methodology is applied to a case study at a busy European airport, using real data from peak traffic months. Results for the scenarios analyzed show that the factors that have a greatest impact on delay and congestion are the level of saturation at arrival processes, the time frame of the day (which determines the arrival declared capacity) and the meteorological conditions. Moreover, arrival states of congestion reduce the airport's ability to maintain optimal performance rates. The model represents an evolution from the traditional corrective and binary vision of performance analysis towards a predictive and multi-state approach. The results can be applied to derive operational strategies and draw conclusions regarding arrival performance and management.