Comparison of gap-based and flow-based control strategies using a new controlled stochastic cellular automaton model for traffic flow

Abstract

Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC.

18 pages, 15 figures

References in corpus (8)

• Traffic and Related Self-Driven Many-Particle Systems
• Statistical Physics of Vehicular Traffic and Some Related Systems
• Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments
• Traffic experiment reveals the nature of car-following
• Jamming transition in a homogeneous one-dimensional system: the Bus Route Model
• A stochastic optimal velocity model and its long-lived metastability
• System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability
• Theoretical conditions for restricting secondary jams in jam-absorption driving scenarios