A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data

Operation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm

: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...

Modeling Attention in Panoramic Video: A Deep Reinforcement Learning Approach

Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a database collecting subjects’ HM positions on panoramic video sequences. From this database, we find that the HM data are highly consistent across subject...

Traffic Diversion Resulting from Ramp Metering

Control of traffic with anticipative ramp metering

When there are different routes from origin to destination in a network, the traffic divides itself over these routes. The resulting assignment is often determined by the travel times on the different routes. When control measures are implemented the travel times for some routes change, which can lead to a change in the assignment. This change in assignment can effect the performance of the who...

Smooth Switching H∞ PI Controller for Local Traffic On-ramp Metering, an LMI Approach

This paper deals with the design of a bank of robust H∞ PI controllers for local traffic control. Traffic flow is modeled by the Uncertain Cell Transmission Model (UCTM) which is an affine switched model. It switches between different operating modes according to the traffic state (state dependent switching) and the uncertain parameters of the system. A robust PI controller is calculated for ea...