1. Demand prediction for urban air mobility using deep learning.
- Author
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Ahmed, Faheem, Memon, Muhammad Ali, Rajab, Khairan, Alshahrani, Hani, Abdalla, Mohamed Elmagzoub, Rajab, Adel, Houe, Raymond, and Shaikh, Asadullah
- Subjects
DEEP learning ,DEMAND forecasting ,FORECASTING ,CHOICE of transportation - 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]
- Published
- 2024
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