A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

Abstract

The random nature of traffic conditions on freeways can cause excessive congestion and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable and practical ramp metering algorithm that considers both critical traffic measures and historical data is still a challenging problem. In this study we use simple machine learning approaches to develop a novel real-time ramp metering algorithm. The proposed algorithm is computationally simple and has minimal data requirements, which makes it practical for real-world applications. We conduct a simulation study to evaluate and compare the proposed approach with an existing traffic-responsive ramp metering algorithm.

Analysis and Evaluation of Ramp Metering: From Historical Evolution to the Application of New Algorithms and Engineering Principles

“…An example is the introduction of the Internet of Things (IoT) and Internet of Vehicles (IoV), which eventually will lead to the widespread everyday use of automated vehicles. As a consequence, many of the studied models might have to be updated, taking this new technology into consideration because its diffusion will cause an inevitable impact on traffic as we know it today [29,30]. If this circumstance might appear frightening and uncanny at first, especially to the less tech-savvy members of the public, it is instead an exciting opportunity to develop challenging future research studies on the subject and improve people's quality of life.…”