Journal · 2026
Predicting Traffic Anomalies and Collision Risks in V2X Systems: A Deep Learning Approach Using LSTM and GNN
Abstract
No abstract has been published for this record yet. The publisher has not deposited one — you can read the paper via its DOI.
Publication details
- Venue
- Open Journal of Safety Science and Technology
- Type
- Journal · 2026
- DOI
- 10.4236/ojsst.2026.163010
- Citations
- 0 · via Crossref, 17 September 2026
Cite this publication
Copy a ready-formatted citation in your preferred style.
APA
Quito, B. (2026). Predicting Traffic Anomalies and Collision Risks in V2X Systems: A Deep Learning Approach Using LSTM and GNN. Open Journal of Safety Science and Technology. https://doi.org/10.4236/ojsst.2026.163010
Harvard
Quito, B. (2026) 'Predicting Traffic Anomalies and Collision Risks in V2X Systems: A Deep Learning Approach Using LSTM and GNN', Open Journal of Safety Science and Technology. doi: 10.4236/ojsst.2026.163010.
IEEE
B. Quito, "Predicting Traffic Anomalies and Collision Risks in V2X Systems: A Deep Learning Approach Using LSTM and GNN," Open Journal of Safety Science and Technology, 2026. doi: 10.4236/ojsst.2026.163010.
Vancouver
Quito B. Predicting Traffic Anomalies and Collision Risks in V2X Systems: A Deep Learning Approach Using LSTM and GNN. Open Journal of Safety Science and Technology. 2026. doi: 10.4236/ojsst.2026.163010.
MLA
Quito, Benjamin. "Predicting Traffic Anomalies and Collision Risks in V2X Systems: A Deep Learning Approach Using LSTM and GNN." Open Journal of Safety Science and Technology, 2026. https://doi.org/10.4236/ojsst.2026.163010.
Chicago
Quito, B.. "Predicting Traffic Anomalies and Collision Risks in V2X Systems: A Deep Learning Approach Using LSTM and GNN." Open Journal of Safety Science and Technology (2026). https://doi.org/10.4236/ojsst.2026.163010.
BibTeX
@article{quito2026, title={Predicting Traffic Anomalies and Collision Risks in V2X Systems: A Deep Learning Approach Using LSTM and GNN}, author={Quito, Benjamin}, journal={Open Journal of Safety Science and Technology}, year={2026}, doi={10.4236/ojsst.2026.163010}, }