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Demand prediction for urban air mobility using deep learning.
- Source :
- PeerJ Computer Science; Apr2024, p1-27, 27p
- Publication Year :
- 2024
-
Abstract
- Urban air mobility, also known as UAM, is currently being researched in a variety of metropolitan regions throughout the world as a potential new mode of transport for travelling shorter distances inside a territory. In this article, we investigate whether or not the market can back the necessary financial commitments to deploy UAM. A challenge in defining and addressing a critical phase of such guidance is called a demand forecast problem. To achieve this goal, a deep learning model for forecasting temporal data is proposed. This model is used to find and study the scientific issues involved. A benchmark dataset of 150,000 records was used for this purpose. Our experiments used different state-of-the-art DL models: LSTM, GRU, and Transformer for UAM demand prediction. The transformer showed a high performance with an RMSE of 0.64, allowing decision-makers to analyze the feasibility and viability of their investments. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
DEMAND forecasting
FORECASTING
CHOICE of transportation
Subjects
Details
- Language :
- English
- ISSN :
- 23765992
- Database :
- Complementary Index
- Journal :
- PeerJ Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 177325788
- Full Text :
- https://doi.org/10.7717/peerj-cs.1946