Neural Forecasting for Urban Logistics Flows
Quelle: https://www.mdpi.com/2071-1050/12/20/8365
Neural Forecasting for Urban Logistics Flows refers to the use of artificial neural networks to predict future patterns of goods movement, delivery demand, and related variables within urban logistics networks. This approach employs deep learning architectures such as recurrent neural networks, convolutional neural networks, or graph neural networks to model spatio-temporal dependencies and estimate volumes, routes, and resource requirements.
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