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Traditional methods often focus only on the overall passenger volume, neglecting the interdependence between different modes within the hub Forecasting can be useful in order to plan both services and trips Deep learning is better suited to managing massive amounts of traffic data and predicting extended time series
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Traditional methods often focus only on the overall passenger volume, neglecting the interdependence between different modes within the hub.
A systematic review on passenger emergency evacuations from multimodal transportation hubs is currently absent thus far Consequently, we have a limited understanding of the demand, theory, and methods for passenger emergency evacuations from multimodal transportation hubs. While numerous research papers concentrate on predicting passenger flow within individual modes of transportation, there is a noticeable lack of emphasis on the intricacies of multimodal systems. In the realm of forecasting passenger flows within multimodal transportation systems that integrate metro services, existing studies have been limited in their scope, lacking comprehensive case studies and thorough method comparisons, including the exploration of method limitations.
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