Ramp metering (RM) dynamically adjusts ramp flow merging into freeway mainline
according to real-time traffic conditions to improve traffic operation. The effectiveness of RM …

Ramp metering that uses traffic signals to regulate vehicle flows from the on‐ramps has
been widely implemented to improve vehicle mobility of the freeway. Previous studies …

We present a novel ramp metering algorithm that incorporates multi-agent deep
reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors …

Most current RM approaches are developed for fixed bottlenecks. However, the number and
locations of bottlenecks are usually uncertain and even time‐varying due to some …

To relieve freeway congestion during peak periods, ramp metering (RM) is often
implemented to control the input flow from onramps on freeways. Many studies focus on …

Ramp metering (RM) is the most effective dynamic traffic measure in response to growing
congestion in urban freeway networks. Among the extensive RM methods available, those …

We proposed a novel ramp metering algorithm embedding multi-agent deep reinforcement
learning (DRL) techniques, based on the data of loop detectors. A multi-agent DRL …

This paper proposes a physics-informed reinforcement learning (RL)-based ramp metering
strategy, which trains the RL model using a combination of historic data and synthetic data …

On freeway corridors, traffic flow is limited by active bottlenecks. Weaving maneuvers (ie,
intensive lane changes) are a major cause of bottlenecks during high-demand periods. To …

Transportation agencies need to assess ramp metering performance when deploying or
expanding a ramp metering system. The evaluation of a ramp metering strategy is primarily …