Advancing safety through connected and autonomous vehicles: a meta-analysis of market penetration and safety improvement rates from 2015 to 2024
File(s)
Published version
Author(s)
Taheri, Amirhossein
Yang, Jing
Müller, Steffen
Milakis, Dimitris
Quddus, Mohammed
Type
Journal Article
Abstract
The field of Connected and Autonomous Vehicles (CAVs) promises to improve road safety through advanced communication technologies and data sharing. This study addresses the need for a clearer understanding of the relationship between CAV Market Penetration Rates (MPRs) and Safety Improvement Rates (SIRs) by conducting an updated meta-analysis that includes 49 studies published between 2015 and 2024. Across these studies, the primary focus of safety surrogate measures (SSMs) was on Time-to-Collision (TTC) and Post Encroachment Time (PET), reflecting their dominant role in evaluating traffic safety impacts of CAVs. We applied sensitivity analysis to refine the data and mitigate the effects of variability across simulation parameters and scenarios. A power function was identified as the most accurate model to describe the MPR-SIR relationship, capturing how safety benefits scale with increasing adoption of CAVs. At low MPRs (10%–20%), safety gains are modest, with SIRs of 2.8% and 4.9%, respectively. Mid-range MPRs (30%–60%), while frequently studied, show overestimated safety benefits in raw findings; our corrected estimates yield SIRs of 8.0% at 30% MPR and 9.4% at 40% MPR, highlighting the complexity of mixed traffic environments. At high MPRs (70%–90%), safety benefits become substantial and more consistent, with an SIR of 39.4% at 90% MPR. These findings inform transportation planners and policymakers on the safety potential of CAVs, emphasizing the importance of high market penetration to realize meaningful safety improvements.
Date Issued
2026-03-04
Date Acceptance
2026-02-13
Citation
European Transport Research Review, 2026, 18
ISSN
1867-0717
Publisher
SpringerOpen
Journal / Book Title
European Transport Research Review
Volume
18
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
9
Date Publish Online
2026-03-04