creexpCrede Experto: транспорт, общество, образование, языкCrede Experto: transport, society, education, language2312-1327Иркутский филиал ФГБОУ ВО «МГТУ ГА»10.51955/2312-1327_2025_3_131creexp-99Research ArticleСИСТЕМЫ НАБЛЮДЕНИЯ И ОРГАНИЗАЦИИ ВОЗДУШНОГО ДВИЖЕНИЯОбнаружение критически важных звеньев в пространственно-временных маршрутах с использованием теории сложных сетейDetection of critical links in spatial-temporal routes based on complex networkshttps://orcid.org/0009-0007-3917-6256ГаспарянГ. А.GasparyanG. A.

Григорий Арменович Гаспарян, аспирант

Кронштадтский б-р, 20, Москва, 125493

Grigory A. Gasparyan, postgraduate student

20, Kronshtadtsky blvd, Moscow, 125493

grigory.rw@gmail.comМосковский государственный технический университет гражданской авиацииРоссияMoscow State Technical University of Civil AviationRussian Federation20252711202503131161Copyright © Гаспарян Г.А., 20252025Гаспарян Г.А.Gasparyan G.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://ce.if-mstuca.ru/jour/article/view/99

В работе предложен усовершенствованный метод выявления критических рёбер в пространственно-временных маршрутных сетях на основе комплексного сетевого анализа. В отличие от ранее предложенных моделей, метод учитывает не только топологические характеристики маршрутов, но и их динамическую нестабильность через комбинированный вес, включающий среднюю скорость движения и её дисперсию. Дополнительно вводятся метрики нагрузки и устойчивости связности. Критические рёбра определяются автоматически через перколяционный анализ, без необходимости ручной настройки порогов. Для прогнозирования критичности используется градиентный бустинг, опирающийся на набор структурных и временных признаков. Предложенный подход обеспечивает более точное, воспроизводимое и адаптивное выявление уязвимых участков в сетях маршрутов и может быть применён в реальном времени для поддержки управления воздушным движением.

This paper presents an enhanced method for detecting critical edges in spatial-temporal route networks based on complex network analysis. Unlike previous models, the proposed approach accounts not only for the topological characteristics of routes but also for their dynamic variability through a composite weight that includes the average travel speed and its variance. Additional metrics, such as load centrality and robustness of connectivity, are introduced. Critical edges are automatically identified via percolation analysis, without the need for manual threshold adjustment. For criticality prediction, a gradient boosting model is employed, using a set of structural and temporal features. The proposed approach ensures more accurate, reproducible, and adaptive identification of vulnerable segments in route networks and can be applied in real time to support air traffic management.

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The authors declare that there are no conflicts of interest present.