Deep reinforcement learning has been extensively studied for traffic signal control owing to its ability of processing large amounts information and achieving superior performance control. However, this method acquires flow-specific policies during learning. Thus, under unexperienced traffic fl...

Because cities worldwide have high population concentration, traffic congestion is a key problem that needs to be addressed. As modern technology advances, smart management able collect data from the environment and uses contextual signal assignment determine flow at intersections improve conditions. In this paper, we propose green time allocation system based on deep Q-network (DQN) can maximi...

Optimizing traffic signals to improve progression relies on minimizing mobility performance measures (e.g., delays and stops). However, delay stop minimizations do not necessarily lead minimal sustainability fuel consumption emissions). For that reason, researchers have focused, for decades, integrating models, signal optimization emissions models minimize metrics while keeping acceptable level...

In order to facilitate and guarantee the safety of vehicular traffic on roadways, control is crucial. Currently, there a lot study how effectively alter parameters lights for aim facilitating road traffic, but observation targets such research are restricted vehicles. Traffic congestion in urban areas severe issue. However, interference between automobiles pedestrians creates actual making vita...