AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Congestion in urban roads is one of the largest dilemmas in contemporary cities which causes an increment in the travel. it is cheaper, less consuming fuel and causing pollution. The standard traffic control systems are cycle-based. Existing intelligent traffic systems rely on manual monitoring and thus could not cope...
Traffic congestion in urban areas is largely caused by traditional traffic signal systems that operate on fixed timing mechanisms without considering real-time traffic conditions. This project proposes an AI-based adaptive traffic signal control system that dynamically adjusts signal timings based on real-time vehicle...
Traffic congestion is a persistent challenge in rapidly urbanizing regions, including Penang Island, Malaysia, where growing vehicle demand increasingly exceeds road capacity. Quantitative understanding of traffic dynamics is therefore essential for evidence-based traffic management and infrastructure planning. While p...
Traffic congestion in Ho Chi Minh City creates an urgent need for real-time traffic flow forecasting systems to support traffic monitoring and management. However, traffic data collected from cameras are often limited, noisy, and difficult to directly exploit for forecasting models. This study proposes an end-to-end re...
The study found a positive relationship between traffic volume and temperature, while traffic volume decreased significantly on weekends, and the models with the highest prediction accuracy were XGBoost, Artificial Neural Network, and Ridge Regression.
Traffic congestion at signalized urban intersections remains a major challenge due to dynamic vehicle volume and limited road capacity. Fixed-time traffic signals often fail to respond to real-time traffic variation, causing inefficient signal distribution and longer vehicle queues. This paper proposes a YOLOv10-based...
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