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Abstract Rapid advances in vehicle automation and communication technologies enable connected autonomous vehicles (CAVs) to cross intersections cooperatively, which could significantly improve traffic throughput and safety at intersections. Virtual platooning, designed upon car‐following behavior, is one of the promising control methods to promote cooperative intersection crossing of CAVs. Nevertheless, demand variation raises safety and stability concerns when CAVs adopt a virtual platooning control approach. Along this line, this study proposes an adaptive vehicle control method to facilitate the formation of a virtual platoon and the cooperative crossing of CAVs, factoring demand variations at an isolated intersection. This study derives the stability conditions of virtual CAV platoons depending on the time‐varying traffic demand. Based on the derived stability conditions, an optimization model is proposed to adaptively control CAVs dynamics by balancing approaching traffic mobility and safety to enhance the reliability of cooperative crossing at intersections. The simulation results show that, compared to the nonadaptive control, our proposed method can increase the intersection throughput by 18.2%. Also, time‐to‐collision results highlight the advantages of the proposed adaptive control in securing traffic safety.
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This content will become publicly available on January 1, 2026
Enhancing Intersection Safety at Uncontrolled Three‐Legged Intersections Through Assessment of Risk
ABSTRACT The accepted gap—the time or distance a driver deems sufficient to enter or cross an intersection—is a key indicator of traffic risk, particularly at uncontrolled three‐legged intersections. Smaller accepted gaps are linked to higher risk due to an increased chance of vehicle conflicts. This study investigates the relationship between accepted gaps and risk and proposes a method to quantify the level of risk and severity (LORS) to guide targeted safety interventions. Data on vehicle speed, accepted gap and critical gap were collected from six rural intersections in India. Using a binary logit regression model and clustering techniques, the LORS was estimated and validated against actual accident data, yielding a predictive accuracy of up to 83%. The significance of this study lies in its novel data‐driven approach to safety assessment using parameters easily measured in the field. Designed for heterogeneous traffic conditions, the method provides traffic engineers and planners with a practical tool to assess intersection safety, recommend specific remedial measures and prioritise interventions based on risk and severity levels. With potential for automation and scalability, this research contributes to the development of safer road systems, particularly in low‐resource settings where conventional crash data is limited or unavailable.
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- Award ID(s):
- 2330565
- PAR ID:
- 10645088
- Publisher / Repository:
- IET Intelligent Transport Systems
- Date Published:
- Journal Name:
- IET Intelligent Transport Systems
- Volume:
- 19
- Issue:
- 1
- ISSN:
- 1751-956X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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