SafeLight: A Reinforcement Learning Method toward Collision-Free Traffic Signal Control

Reinforcement learning in neurofuzzy traffic signal control

Reinforcement Learning For Adaptive Traffic Signal Control

By 2050, two-thirds of the world’s 9.6 billion people will live in urban areas [2]. In many cities, opportunities to expand urban road networks are limited, so existing roads will need to more efficiently accommodate higher volumes of traffic. Consequently, there is a pressing need for technologically viable, low-cost solutions that can work with existing infrastructure to help alleviate increa...

Using a Deep Reinforcement Learning Agent for Traffic Signal Control

Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. We propose a traffic signal control system which takes advantage of this new, high quality data, with minimal abstraction compared to other proposed systems. We apply modern deep...

A reinforcement learning based traffic signal control algorithm in a connected vehicle environment

The emerging vehicle technologies, i.e. connected vehicle technology and autonomous driving technology, can be beneficial for traffic control and operations. They not only serve as new source of information, but also enable us to control and modify the trajectory of vehicles. Reinforcement learning is widely used to design intelligent control algorithms in various disciplines. This paper provid...

Real-time Reinforcement Learning in Traffic Signal System

Real-time optimization of a traffic signal system is a difficult decision-making problem with no considerable model given. To achieve this optimization, a perfect tradeoff should be made between the distributed learning in each individual intersection, and the integrated cooperation in the whole traffic grid. This paper explores such a tradeoff and designs an efficient traffic signal system by ...