Explainable reinforcement learning for improved traffic signal control
Abstract Reinforcement learning has become a popular approach for traffic signal control, but its complexity often hinders practical application. To improve this, we propose a deep Q‐network framework with an attention mechanism inspired by how traffic police manage intersections.
Yuan Hu, Longfei Du, Said M. Easa
wiley +1 more source
Performance and Energy Consumption Analysis for UWSNs with Priority Scheduling Based on Access Probability and Wakeup Threshold. [PDF]
Li N +5 more
europepmc +1 more source
Annotation‐Guided AoS‐to‐SoA Conversions and GPU Offloading With Data Views in C++
ABSTRACT The C++ programming language provides classes and structs as fundamental modeling entities. Consequently, C++ code tends to favor array‐of‐structs (AoS) for encoding data sequences, even though structure‐of‐arrays (SoA) yields better performance for some calculations.
Pawel K. Radtke, Tobias Weinzierl
wiley +1 more source
An integrated model of threshold-based scaling and fractional admission controlling to improve resource utilization efficiency in 5G core networks. [PDF]
Hoa LC, Dang TC, Vo VMN.
europepmc +1 more source
The 85% bed occupancy fallacy: The use, misuse and insights of queuing theory
Nathan Proudlove
openalex +2 more sources
ABSTRACT Autonomous vehicles (AVs) are one of the building blocks of modern intelligent transportation systems and have the potential to change some aspects related to mobility, safety, and operational efficiency. In this paper, we analyze recent progress in AV algorithms and simulation frameworks, emphasizing their roles in decision‐making processes ...
Majd Alkorabi +2 more
wiley +1 more source
A Novel Traffic Scheduling Algorithm for Multi-CQF Using Mixed Integer Programming and Variable Neighborhood Search Genetic Algorithm in Time-Sensitive Networking. [PDF]
Wang C, Lin Z, Zhao Y, Hu F, Huan Z.
europepmc +1 more source
Decision Support System Based on Queuing Theory to Optimize Canal Management
Gaiqiang Yang, Mo Li, Lijuan Huo
openalex +2 more sources
The Promise and Pitfalls of Artificial Intelligence in the Evaluation of Synthetic “Greenness”
Rapid rise of AI prompts a question whether it can be used to accelerate and standardize assessment of synthetic “greenness.” This perspective argues for the potential of such applications, although it also identifies several hurdles, like the need for extensive training or the creation of “ground truth” datasets.
Paweł Mateusz Nowak +3 more
wiley +1 more source

