The public transportation system is now dealing with a number of problems brought on by
the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a …

Deep reinforcement learning methods have shown promising results in the development of
adaptive traffic signal controllers. Accidents, weather conditions, or special events all have …

Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic
flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light …

Traffic congestion remains a significant challenge in urban management, with traditional
fixed-cycle traffic signal systems struggling to adapt to dynamic traffic conditions. This paper …

Driving decision-making plays an important role in determining the rationality and safety of
automated commercial vehicles. Unlike extensively studied passenger vehicles, commercial …

La investigación tuvo como objetivo, realizar la propuesta de un sistema adaptativo para
intersecciones semaforizadas en la ciudad de Jaén, basado en algoritmos y ejecución de …

Traffic forecasting is essential for the intelligent transportation system and is applied to
managing traffic networks. Recently, there has been a growing interest in incorporating …

Managing the traffic system is a multifaceted challenge, imposing various solutions to
prevent congestion. Traffic management aims to create an efficient control system with …

This paper investigates the impact of adding exclusive two-wheeled vehicle lane based on
motorcycle data and using reinforcement learning at one of Bandung's heterogeneous …