The Air Traffic Management System is characterized by a highly technological environment in which massive amounts of data are generated daily from multiple sources as planning/operations occur. The development of analytics tools that tap into this big data potential can significantly contribute to improve future operations through two primary ways: post-event analysis of operational performance that identifies inefficiencies and provides guidance in offline system adjustments and characterization of actual system behavior that feeds data-driven models for real time decision support. This work contributes to these goals by developing a comprehensive data mining framework for characterization of air traffic flows based on recorded radar tracks. A density-based clustering algorithm is used to identify major flight trajectory patterns in the airspace. A posterior ensemble-based classification scheme enables the assignment of flight trajectories from datasets of any size to the learned patterns and detects non-conforming behaviors. Finally, operational modes of airspace use can be extracted based on an innovative compact representation of daily air traffic flows. In order to illustrate the outcomes and potential applications of the proposed framework, an assessment of air traffic control operations in the transition/terminal airspace was performed for the New York Metro region. We identified major nominal and rerouting patterns for weather avoidance, assessed the performance of tactical operations in a daily basis and evaluated how capacity constraints imposed by convective weather impact system behavior in aggregate and route based perspectives.

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Purpose A common way of eliciting speech from individuals is by using passages of written language that are intended to be read aloud. Read passages afford the opportunity for increased control over the phonetic properties of elicited speech, of which phonetic balance is an often-noted example. No comprehensive analysis of the phonetic balance of read passages has been reported in the literature. The present article provides a quantitative comparison of the phonetic balance of widely used passages in English. Method Assessment of phonetic balance is carried out by comparing the distribution of phonemes in several passages to distributions consistent with typical spoken English. Data regarding the distribution of phonemes in spoken American English are aggregated from the published literature and large speech corpora. Phoneme distributions are compared using Spearman rank order correlation coefficient to quantify similarities of phoneme counts in those sources. Results Correlations between phoneme distributions in read passages and aggregated material representative of spoken American English ranged from .70 to .89. Correlations between phoneme counts from all passages, literature sources, and corpus sources ranged from .55 to .99. All correlations were statistically significant at the Bonferroni-adjusted level. Conclusions Passages considered in the present work provide high, but not ideal, phonetic balance. Space exists for the creation of new passages that more closely match the phoneme distributions observed in spoken American English. The Caterpillar provided the best phonetic balance, but phoneme distributions in all considered materials were highly similar to each other.

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