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Enhanced forecasting of online car-hailing demand using an improved empirical mode decomposition with long short-term memory neural network.
- Source :
-
Transportation Letters . Feb2024, p1-17. 17p. 18 Illustrations, 8 Charts. - Publication Year :
- 2024
-
Abstract
- The study on forecasting demand for online car-hailing holds substantial implications for both online car-hailing platforms and government agencies responsible for traffic management. This research proposes an enhanced Empirical Mode Decomposition Long-short Term Memory Neural Network (EMD-LSTM) model. EMD technique reduces noise and extracts stable intrinsic mode functions (IMF) from the original time series. Genetic algorithm is deployed to improve the K-Means clustering for determining optimal clusters. These sub time series serve as input for the prediction model, with combined results giving final predictions. Experimental data from Didi includes Haikou’s car-hailing orders from May to October 2017 and Beijing’s from January to May 2020. Results show improved EMD-LSTM reduces instability and captures characteristics better. Compared to unmodified EMD-LSTM, RMSE decreases by 3.50%, 6.81%, and 6.81% for the three datasets, and by 30.97%, 20%, and 9.24% respectively compared to single LSTM model. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19427867
- Database :
- Academic Search Index
- Journal :
- Transportation Letters
- Publication Type :
- Academic Journal
- Accession number :
- 175316468
- Full Text :
- https://doi.org/10.1080/19427867.2024.2313832