Enhancing Traffic Speed Prediction Accuracy: The Multialgorithmic Ensemble Model With Spatiotemporal Feature Engineering
Accurate traffic speed prediction is crucial for efficient traffic management and planning in urban areas. Traditional traffic prediction models often fall short due to their inability to capture the complex and dynamic nature of traffic flow. There is a need for more advanced models that can effect...
Saved in:
| Main Authors: | Ali Ardestani, Hao Yang, Saiedeh Razavi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/9941856 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories.
by: Hao Li, et al.
Published: (2025-01-01) -
Enhancing financial product forecasting accuracy using EMD and feature selection with ensemble models
by: Eddy Suprihadi, et al.
Published: (2025-06-01) -
Unifying spatiotemporal and frequential attention for traffic prediction
by: Qi Guo, et al.
Published: (2025-01-01) -
Prediction of Promotors in Agrobacterium and Klebsiella Using Novel Feature Engineering and Ensemble Learning Approach
by: Nagwan Abdel Samee, et al.
Published: (2025-01-01) -
Ensemble Methods for Peristaltic Pump Accuracy Enhancement
by: Davide Privitera, et al.
Published: (2025-01-01)