УПРАВЛІННЯ ЛОГІСТИКОЮ ВІДПОВІДНО ДО ПОПИТУ НА ПАСАЖИРСЬКІ ТА ВАНТАЖНІ ПЕРЕВЕЗЕННЯ З ПОЯВОЮ ШТУЧНОГО ІНТЕЛЕКТУ (ДОСВІД ДЛЯ УКРАЇНИ)
Ключові слова:
штучний інтелект, транспортно-логістична галузь, прогнозування попиту, оптимізація технолого-економічних процесів, глибоке машинне навчання.
Анотація
Вступ. Дослідження спрямоване на визначення потенційної можливості оптимізації транспортно-логістичної галузі України за допомогою штучного інтелекту. Реалізація системних організаційних рішень на основі штучного інтелекту, зокрема для прогнозування та задоволення попиту на пасажиро- та вантажоперевезення, може сприяти технологоекономічній оптимізації галузі. Мета. Метою статті є проведення прогнозно-статистичного оцінювання впливу системних рішень з інтеграції штучного інтелекту в транспортно-логістичну галузь України. Використання системних моделей та інструментів штучного інтелекту, що ґрунтуються на глибокому машинному навчанні, дає можливість прогнозувати та оптимізувати розвиток галузі. Результати. Результати дослідження вказують на потенційну можливість впровадження системних рішень з використанням штучного інтелекту в транспортно-логістичній галузі України. Ці рішення можуть призвести до позитивних зрушень в транспортній сфері, включаючи можливість зупинити тривалий період рецесії. Результати дослідження мають практичні наслідки для формування механізмів та організаційно-технологічних рішень з впровадженням новітніх технологій четвертої хвилі індустріального розвитку та глобальної цифровізації. Висновки. На основі проведеного аналізу та моделювання прогнозів можна зробити висновок, що впровадження штучного інтелекту в транспортно-логістичну галузь України має потенціал для покращення ефективності та оптимізації процесів пасажиро- та вантажоперевезень. Запропонована модель і, зокрема, інструментарій застосування засобів штучного інтелекту на основі глибокого машинного навчання, можуть стати основою для подальшого розвитку транспортної галузі України. В майбутньому важливо провести додаткові дослідження, для подальшого розвитку моделі прогнозування та оптимізації, зокрема для створення більш точних моделей прогнозування попиту на пасажирські та вантажні перевезення. Розвиток технологій штучного інтелекту та використання нових алгоритмів машинного навчання можуть допомогти вдосконалити прогнозні моделі та їх застосування в транспортно-логістичній галузі.
Завантаження
Дані завантаження ще не доступні.
Посилання
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33. Nigam R., Govinda K. Cloud based flight delay prediction using logistic regression. 2017 international conference on intelligent sustainable systems (ICISS), Palladam, 7–8 December 2017. 2017. URL: https://doi. org/10.1109/iss1.2017.8389254 (date of access: 08.07.2023).
Опубліковано
2023-10-12
Як цитувати
Harkava, V. (2023). УПРАВЛІННЯ ЛОГІСТИКОЮ ВІДПОВІДНО ДО ПОПИТУ НА ПАСАЖИРСЬКІ ТА ВАНТАЖНІ ПЕРЕВЕЗЕННЯ З ПОЯВОЮ ШТУЧНОГО ІНТЕЛЕКТУ (ДОСВІД ДЛЯ УКРАЇНИ). Розвиток транспорту, (3(18), 9-23. https://doi.org/10.33082/td.2023.3-18.01
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