JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT 2782-4586 2949-1851 106322 10.26118/2782-4586.2025.90.11.016 Научные статьи SCIENTIFIC ARTICLES Научные статьи A combined forecasting model to improve the economic efficiency of regional passenger transportation Комбинированная модель прогнозирования для повышения экономической эффективности региональных пассажирских перевозок https://orcid.org/0000-0001-6880-0897 Иванова Любовь Николаевна Ivanova Lubov Nikolaevna 45is@mail.ru Ананченко Игорь Викторович Ananchenko Igor' Viktorovich anantchenko@yandex.ru

кандидат технических наук;

candidate of technical sciences;

 Зудилова Татьяна Викторовна Zudilova Tat'yana Viktorovna

кандидат технических наук;

candidate of technical sciences;

 Санкт-Петербургский морской государственный технический университет Санкт-Петербург Россия Saint Petersburg Maritime State Technical University Saint-Petersburg Russian Federation Федеральное государственное бюджетное образовательное учреждение высшего образования "Санкт-Петербургский государственный технологический институт (технический университет)" Россия Federal State Budgetary Educational Institution of Higher Education "St. Petersburg State Technological Institute (Technical University)" Russian Federation Национальный исследовательский университет информационных технологий, механики и оптики National Research University of Informational Technologies, Mechanics and Optics 18 01 2026 18 01 2026 12 137 146 10 11 2025 https://zhpi.ru/en/nauka/article/106322/view

В работе предложен новый метод для прогнозирования пассажиропотока с применением комбинированной модели. Эффективное развитие регионального транспортного сектора экономики зависит от прогнозирования пассажиропотока. Для повышения качества пассажирских перевозок необходимо построение логистических моделей и разработка точных методов для прогнозирования. Решаемая задача прогнозирования пассажиропотока особенно актуальна в условиях ускоренного роста населения и туристического потока, расширения городских границ, развития инфраструктуры. Качественное прогнозирование пассажирского потока позволит эффективно управлять транспортом, снижать затраты. Точный прогноз пассажирского потока обеспечивает комфортные условия для пассажиров. Для точного прогнозирования в работе предложен новый комбинированный метод, объединяющий преимущества различных моделей прогнозирования: градиентного бустирования, ансамбля деревьев решений и метода Холта-Уинтерса. Для комбинированной модели выполняется оптимальный выбор весов на основе характеристик моделей. Для вычислительного эксперимента авторами разработана программа на Python. Результаты эксперимента показали повышение точности прогнозирования комбинированной модели на 15% по сравнению с градиентным бустированием, и на 19% по сравнению с экспоненциальным сглаживанием.

This paper proposes a new method for forecasting passenger traffic using a combined model. Effective development of the regional transport sector depends on passenger traffic forecasting. Improving the quality of passenger transportation requires building logistics models and developing accurate forecasting methods. Passenger traffic forecasting is particularly relevant in the context of accelerated population and tourist growth, expanding city boundaries, and infrastructure development. High-quality passenger traffic forecasting enables efficient transport management and cost reduction. Accurate passenger traffic forecasting ensures comfortable conditions for passengers. To achieve accurate forecasting, this paper proposes a new combined method that combines the advantages of various forecasting models: gradient boosting, decision tree ensemble, and the Holt-Winters method. For the combined model, optimal weight selection is performed based on the model characteristics. The authors developed a Python program for the computational experiment. The results of the experiment showed a 15% increase in forecasting accuracy for the combined model compared to gradient boosting and a 19% increase compared to exponential smoothing.

 пассажирские перевозки прогнозирование пассажиропотока комбинированная модель машинное обучение повышение эффективности passenger transportation passenger flow forecasting combined model machine learning efficiency improvement нет

 

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