Air cargo transport demand forecasting using ConvLSTM2D, an artificial neural network architecture approach-Reference-Cited by-同舟云学术

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Air cargo transport demand forecasting using ConvLSTM2D, an artificial neural network architecture approach

Published:2023-06 Issue: Volume:12 Page:101009
ISSN:2213-624X
Container-title:Case Studies on Transport Policy
language:en
Short-container-title:Case Studies on Transport Policy

Author:

Gerardo Muros Anguita Juan,Díaz Olariaga Oscar

Publisher

Elsevier BV

Subject

Urban Studies,Transportation,Geography, Planning and Development

Reference87 articles.

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2. Annual World Airport Traffic Report;Aci,2021

3. Aerocivil (2022). https://www.aerocivil.gov.co/atencion/estadisticas-de-las-actividades-aeronauticas/boletines-operacionales.

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5. Neural Networks and Deep Learning;Aggarwal,2018

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1. Strategic Decisions and Policies on Türkiye-Europe Air Cargo Transport;Journal of Aviation;2023-11-15

2. Simulation Model for Sustainable Management of the Air Cargo Screening Process;Energies;2023-10-25

3. An active learning convolutional neural network for predicting river flow in a human impacted system;Frontiers in Water;2023-10-17

4. Analysis on the impacts of changes in China's peripheral diplomatic relations on export trade using integrated ARIMA–LSTM model;Soft Computing;2023-09-25

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