Passenger-Oriented Enhanced Metrics

We report on a project building the first European ATM simulation combining flight and passenger trip data. New propagation-centric and passenger-centric performance metrics are described. The new metrics will be compared with existing, classical metrics, to compare their respective intelligibility, sensitivity and consistency. The trade-offs in performance across the metrics under a range of flight and passenger prioritisation scenarios will be examined. The corresponding regulatory and socio-political contexts are described. Complexity science techniques demonstrate the need to extend flight-centric network representations to include the passenger perspective

Delay propagation – new metrics, new insights

Network delay propagation is intimately linked with the challenges of managing passenger itineraries and corresponding connections. Airline decision-making governing these processes is driven by operational and regulatory factors. Using the first European network simulation model with explicit passenger itineraries and full delay cost estimations, we explore these factors through various flight and passenger prioritisation rules, assessing the performance impacts. Delay propagation is further characterised under the different prioritisation rules using complexity science techniques such as percolation theory and network attack. The relative effects of randomised and targeted disruption are compared

New perspectives for air transport performance

The average delays of flights and passengers are not the same. The air transport industry is lacking passenger-centric metrics; its reporting is flight-centric. We report on the first European network simulation model with explicit passenger itineraries and full delay cost estimations. Trade-offs in performance are assessed using passenger-centric and flight-centric metrics, under a range of novel flight and passenger prioritisation scenarios. The need for passenger-centric metrics is established. Delay propagation is characterised under the scenarios using, inter alia, Granger causality techniques

NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection

We describe the R package NonpModelCheck for hypothesis testing and variable selection in nonparametric regression. This package implements functions to perform hypothesis testing for the significance of a predictor or a group of predictors in a fully nonparametric heteroscedastic regression model using high-dimensional one-way ANOVA. Based on the p values from the test of each covariate, three different algorithms allow the user to perform variable selection using false discovery rate corrections. A function for classical local polynomial regression is implemented for the multivariate context, where the degree of the polynomial can be as large as needed and bandwidth selection strategies are built in