The impact of automation and connectivity on traffic flow and CO2 emissions. A detailed microsimulation study

TL;DR

This study uses detailed microsimulation to evaluate how connected and automated vehicles influence traffic flow and CO2 emissions, emphasizing realistic vehicle dynamics and driver behavior for accurate results.

Contribution

It introduces a scenario-based microsimulation approach that incorporates realistic vehicle dynamics and emissions modeling to assess CAVs and AVs impacts.

Findings

Driving behavior significantly affects emissions during rush hours

Detailed vehicle dynamics alter the estimated emissions impact

Connectivity and automation influence traffic flow and emissions

Abstract

Many of the anticipated advantages of connected and automated vehicles or automated vehicles without connectivity (CAVs and AVs respectively) on congestion and energy consumption are questionable. Some studies provide quantitative answers to the above questions through microsimulation but they systematically ignore the realistic simulation of vehicle dynamics, driver behaviour or instantaneous emissions estimates, mostly due to the overall increased complexity of the transport systems and the need for low computational demand on large-scale simulations. However, recent studies question the capability of common car-following models to produce realistic vehicle dynamics or driving behaviour, which directly impacts emissions estimations as well. This work presents a microsimulation study that contributes on the topic, using a scenario-based approach to give insights regarding the impact of CAVs and AVs on the evolution of emissions over a highway network. The motivation here is to answer whether the different driving behaviours produce significant differences in emissions during rush hours, and how significant is the impact of detailed vehicle dynamics simulation and instantaneous emissions in the outcome.