| Issue |
Sci. Tech. Energ. Transition
Volume 81, 2026
|
|
|---|---|---|
| Article Number | 2 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.2516/stet/2026002 | |
| Published online | 6 mars 2026 | |
Regular Article
Impact of connected vehicles on the traffic efficiency and PM emissions of mixed traffic flows
Intelligent Social Computing Lab, College of Physics and Electronic Engineering, Sichuan Normal University, Chengdu 610101, PR China
* Corresponding author: Cette adresse e-mail est protégée contre les robots spammeurs. Vous devez activer le JavaScript pour la visualiser.
Received:
17
July
2024
Accepted:
27
January
2026
Abstract
With the rapid development of Connected Vehicles (CVs), it is inevitable that Human-driven Vehicles (HVs) and CVs run on the same road in the near future. Besides the traffic efficiency, the sustainable development in society has become more and more concerned with Particulate Matter (PM) emissions very recently. In this work, mixed traffic flows with both CVs and HVs on a two-lane expressway are proposed; the effects of CV penetration rate on the traffic flow, and the PM emission are the focus. The results of the numerical simulation show that the traffic flow is effectively improved as the proportion of CVs increases. However, there are two stages of PM emissions. When the density is lower, the greater the proportion of CVs, the lower the PM emissions. When the density is higher, the situation reverses. Furthermore, traffic flows in two lanes are not the same, and the symmetry is terribly broken when the penetration of CVs is high. In terms of the forward observable interval of CVs, it can limit PM emissions and increase traffic flow. Thus, a high traffic efficiency and a low PM emission require a high CV penetration but an appropriate observable interval.
Key words: Connected vehicles / Human-driven vehicles / Mixed traffic flows / Particulate Matter (PM) emissions
© The Author(s), published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.
