[HTML][HTML] Unlocking the full potential of deep learning in traffic forecasting through road network representations: A critical review
P Fafoutellis, EI Vlahogianni - Data Science for Transportation, 2023 - Springer
Research in short-term traffic forecasting has been blooming in recent years due to its
significant implications in traffic management and intelligent transportation systems. The …
significant implications in traffic management and intelligent transportation systems. The …
Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model
J Dogani, F Khunjush - Journal of Parallel and Distributed Computing, 2024 - Elsevier
Edge computing has emerged as an attractive alternative to traditional cloud computing by
utilizing processing, network, and storage resources close to end devices, such as Internet …
utilizing processing, network, and storage resources close to end devices, such as Internet …
Appraising machine and deep learning techniques for traffic conflict prediction with class imbalance
Predicting traffic conflicts is pivotal for vehicle-based active safety system to prevent crashes.
Yet, conflict prediction is a challenging task as correct prediction depends on the nature of …
Yet, conflict prediction is a challenging task as correct prediction depends on the nature of …
A review of deep learning-based approaches and use cases for traffic prediction
Rapid population growth with increasing urban-centric activities have imposed a massive
demand on urban transportation systems—leading to increased mobility, reduced safety …
demand on urban transportation systems—leading to increased mobility, reduced safety …