Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals.
- DOI
- 10.3390/s25061664
- Published
- 2025 Mar 7
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s25061664,
title = {Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals.},
author = {Eteifa S and Shafik A and Eldardiry H and Rakha HA},
year = {2025},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s25061664},
url = {https://doi.org/10.3390/s25061664}
}RIS
TY - JOUR TI - Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals. AU - Eteifa S AU - Shafik A AU - Eldardiry H AU - Rakha HA PY - 2025 JO - Sensors (Basel, Switzerland) DO - 10.3390/s25061664 UR - https://doi.org/10.3390/s25061664 ER -
APA
S, E., A, S., H, E., & HA, R. (2025). Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s25061664
Source records
- pubmed · retrieved 2026-11-10T13:25:47.991Z