Integrated Analysis of Coarse-Grained Guidance for Traffic Flow Stability
Sirui Li, Roy Dong, Cathy Wu

TL;DR
This paper develops a theoretical framework using Lyapunov stability to analyze how coarse-grained guidance for human drivers can stabilize traffic flow, providing conditions and insights for designing effective traffic control strategies.
Contribution
It presents the first integrated theoretical analysis linking guidance instructions to traffic stability, extending to human behaviors and offering design insights.
Findings
Derived sufficient stability conditions for guidance hold length Δ
Extended analysis to include human reaction delay and error
Numerical simulations confirm theoretical predictions
Abstract
Autonomous vehicles (AVs) enable more efficient and sustainable transportation systems. Ample studies have shown that controlling a small fraction of AVs can smooth traffic flow and mitigate traffic congestion. However, deploying AVs in real-world systems poses challenges due to safety and cost concerns. A viable alternative approach that can be implemented in the near future is , where human drivers are guided by real-time instructions, updated every seconds, to stabilize the traffic. While previous theoretical studies consider stability analysis for continuous AV control, this article presents the first integrated theoretical analysis that directly relates the guidance provided to the human drivers to the traffic flow stability outcome. Casting the problem into the Lyapunov stability framework, this study derives sufficient conditions for…
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Taxonomy
TopicsTraffic control and management · Autonomous Vehicle Technology and Safety · Automotive and Human Injury Biomechanics
