# On the Traffic Impacts of Optimally Controlled Connected and Automated Vehicles

## TL;DR

This paper investigates how optimally controlling connected and automated vehicles (CAVs) can significantly improve traffic flow and network performance compared to traditional human-driven vehicles.

## Contribution

It introduces a decentralized optimal control framework for CAVs and analyzes its impact on overall transportation network performance.

## Key findings

- CAVs increase roadway capacity substantially
- Network performance improves with CAV deployment
- Decentralized control outperforms baseline scenarios

## Abstract

The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. Several research efforts reported in the literature to date have proposed decentralized control algorithms to coordinate CAVs in various traffic scenarios, e.g., highway on-ramps, intersections, and roundabouts. However, the impact of optimally coordinating CAVs on the performance of a transportation network has not been thoroughly analyzed yet. In this paper, we apply a decentralized optimal control framework in a transportation network and compare its performance to a baseline scenario consisting of human-driven vehicles. We show that introducing of CAVs yields radically improved roadway capacity and network performance.