Iterative Learning Control for Ramp Metering on Service Station On-ramps

TL;DR

This paper proposes an Iterative Learning Control approach based on the Cell Transmission Model with service stations to optimize ramp metering at highway on-ramps, effectively reducing congestion by leveraging recurring traffic patterns.

Contribution

It introduces a novel ILC method tailored for ramp metering using the CTM-s model, capable of compensating for model inaccuracies with historical data.

Findings

ILC effectively manages ramp metering to reduce congestion.

The approach leverages recurring traffic patterns for improved control.

Model inaccuracies are mitigated through iterative learning.

Abstract

Congestion on highways has become a significant social problem due to the increasing number of vehicles, leading to considerable waste of time and pollution. Regulating the outflow from the Service Station can help alleviate this congestion. Notably, traffic flows follow recurring patterns over days and weeks, allowing for the application of Iterative Learning Control (ILC). Building on these insights, we propose an ILC approach based on the Cell Transmission Model with service stations (CTM-s). It is shown that ILC can effectively compensate for potential inaccuracies in model parameter estimates by leveraging historical data.