Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles

Amolika Sinha; Sai Chand; Kasun P Wijayaratna; Navreet Virdi; Vinayak Dixit

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

Accid Anal Prev. 2020 Jul:142:105567. doi: 10.1016/j.aap.2020.105567. Epub 2020 Apr 29.

Authors

Amolika Sinha¹, Sai Chand², Kasun P Wijayaratna³, Navreet Virdi⁴, Vinayak Dixit⁵

Affiliations

¹ Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, UNSW Sydney, Sydney NSW 2052, Australia. Electronic address: amolika.sinha@student.unsw.edu.au.
² Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, UNSW Sydney, Sydney NSW 2052, Australia. Electronic address: saichand.chakka@unsw.edu.au.
³ School of Civil and Environmental Engineering, University of Technology Sydney, Sydney NSW 2007, Australia. Electronic address: Kasun.Wijayaratna@uts.edu.au.
⁴ Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, UNSW Sydney, Sydney NSW 2052, Australia. Electronic address: n.virdi@student.unsw.edu.au.
⁵ Research Centre for Integrated Transport Innovation (rCITI), Professor, School of Civil and Environmental Engineering, UNSW Sydney, Sydney NSW 2052, Australia. Electronic address: v.dixit@unsw.edu.au.

PMID: 32361477
DOI: 10.1016/j.aap.2020.105567

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.

MeSH terms

Acceleration
Accidents, Traffic / prevention & control
Accidents, Traffic / statistics & numerical data*
Automobile Driving*
Humans
Man-Machine Systems*
Motor Vehicles / classification*
Safety
Victoria