Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Based on the literature, we present an artificial intelligence marketing (AIM) framework that
enables autonomous machines to receive big data and information, use artificial intelligence …

While real-world applications of reinforcement learning (RL) are becoming popular, the
security and robustness of RL systems are worthy of more attention and exploration. In …

Solving link-based route guidance problems for large-scale networks is computationally
challenging and faces practical issues, such as spatial–temporal data coverage. Thus …

The number of road accidents has constantly been increasing recently around the world. As
per the national highway traffic safety administration's investigation, 45% of vehicle crashes …

Traffic management is improved in cutting-edge smart cities using technologies such as
machine learning and deep learning to streamline daily tasks and boost productivity …

Recent advances in deep reinforcement learning have shown promising results in solving
sophisticated control problems with high dimensional states and action space. Inspired by …

This paper introduces a library for cross-simulator comparison of reinforcement learning
models in traffic signal control tasks. This library is developed to implement recent state-of …

For the emergency response, every second counts. Intersections are prone to congestion,
which greatly hinders the fast response of emergency vehicles. Although emergency …

Intelligent Transportation has seen significant advancements with Deep Learning and the
Internet of Things, making Traffic Signal Control (TSC) research crucial for reducing …