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Found 5 papers, 1 papers with code
Multi-hop Upstream Preemptive Traffic Signal Control with Deep Reinforcement Learning
Traffic signal control is crucial for managing congestion in urban networks.
Generalized Multi-hop Traffic Pressure for Heterogeneous Traffic Perimeter Control
To assist with this modulation, we can leverage the concept of 1-hop traffic pressure to measure intersection-level traffic congestion.
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction
This suggests that the current graph convolutional methods may not be the best approach to traffic prediction and there is still room for improvement.
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Moreover, no existing studies have employed reinforcement learning for homogeneous flow rate optimization in microscopic simulation, where spatial characteristics, vehicle-level information, and metering realizations -- often overlooked in macroscopic simulations -- are taken into account.
Tutorial: Complexity analysis of Singular Value Decomposition and its variants
We compared the regular Singular Value Decomposition (SVD), truncated SVD, Krylov method and Randomized PCA, in terms of time and space complexity.
