Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles
- PMID: 32361477
- DOI: 10.1016/j.aap.2020.105567
Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles
Abstract
Connected and Automated Vehicle (CAV) technology, although in the development stage, is quickly expanding throughout the vehicle market. However, full market penetration will most likely require considerable planning as key stakeholders, manufacturers, consumers and governing agencies work together to determine optimal deployment strategies. Specifically, road safety is a critical challenge to the widespread deployment and adoption of this disruptive technology. During the transition period fleets will be composed of a combination of CAVs and conventional vehicles, and therefore it is imperative to investigate the repercussions of CAVs on traffic safety at different penetration rates. Since crash severity and frequency in conjunction reflect traffic safety, this study attempts to investigate the effect of CAVs on both crash severity and frequency through a microsimulation modelling exercise. VISSM microsimulation platform is used to simulate a case study of the M1 Geelong Ring Road network (Princes Freeway) in Victoria, Australia. Network performance is evaluated using performance metrics (Total System Travel Time, Delay) and kinematic variables (Speed, acceleration, jerk rate). Surrogate safety measures (time to collision, post encroachment time, etc.) are examined to inspect the safety in the network. The results indicate that the introduction of CAVs does not achieve the expected decrease in crash severity and rates involving manual vehicles, despite the improvement in network performance, given the demand and the set of parameters used in our operational CAV algorithm are intact. Additionally, the study identifies that the safety benefits of CAVs are not proportional to CAV penetration, and full-scale benefits of CAVs can only be achieved at 100 % CAV penetration. Further, considering network efficiency as a performance metric and total crash rate involving conventional vehicles as a safety metric, a Pareto frontier is extracted, for varying CAV operational behaviour. The results presented in this study provide insights into the impacts of CAVs on traffic safety valuable for insurance companies and other industry participants, enabling safety-related services and more enterprising business models.
Keywords: Connected and automated vehicles; Crash rate; Crash severity; Safety; Simulation.
Copyright © 2020 Elsevier Ltd. All rights reserved.
Similar articles
-
Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles.Accid Anal Prev. 2024 Feb;195:107424. doi: 10.1016/j.aap.2023.107424. Epub 2023 Dec 12. Accid Anal Prev. 2024. PMID: 38091887
-
Evaluating the safety impact of connected and autonomous vehicles on motorways.Accid Anal Prev. 2019 Mar;124:12-22. doi: 10.1016/j.aap.2018.12.019. Epub 2019 Jan 2. Accid Anal Prev. 2019. PMID: 30610995
-
A safety assessment of mixed fleets with Connected and Autonomous Vehicles using the Surrogate Safety Assessment Module.Accid Anal Prev. 2019 Oct;131:95-111. doi: 10.1016/j.aap.2019.06.001. Epub 2019 Jun 22. Accid Anal Prev. 2019. PMID: 31233998
-
Examining road safety impacts of Green Light Optimal Speed Advisory (GLOSA) system.Accid Anal Prev. 2024 Jun;200:107534. doi: 10.1016/j.aap.2024.107534. Epub 2024 Mar 28. Accid Anal Prev. 2024. PMID: 38552346 Review.
-
Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways.Accid Anal Prev. 2023 Jun;186:107021. doi: 10.1016/j.aap.2023.107021. Epub 2023 Mar 23. Accid Anal Prev. 2023. PMID: 36965209 Review.
Cited by
-
Crash and disengagement data of autonomous vehicles on public roads in California.Sci Data. 2021 Nov 23;8(1):298. doi: 10.1038/s41597-021-01083-7. Sci Data. 2021. PMID: 34815404 Free PMC article.
-
Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers' Driving Styles.Hum Factors. 2024 Feb;66(2):544-561. doi: 10.1177/00187208221088358. Epub 2022 Apr 25. Hum Factors. 2024. PMID: 35469464 Free PMC article.
-
Multi-year survey data on public opinion on driverless vehicles in Australia and New Zealand.Data Brief. 2025 Jan 10;59:111273. doi: 10.1016/j.dib.2025.111273. eCollection 2025 Apr. Data Brief. 2025. PMID: 39968406 Free PMC article.
MeSH terms
LinkOut - more resources
Full Text Sources