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

TL;DR

This paper introduces a new stochastic cellular automaton model for traffic flow and compares gap-based and flow-based vehicle control strategies, finding that flow-based control consistently improves traffic stability and flow.

Contribution

The study presents the controlled stochastic optimal velocity (CSOV) model and compares two novel control strategies within it, highlighting the robustness of flow-based control.

Findings

Flow-based control consistently enhances traffic flow.

Gap-based control can underperform under weaker control conditions.

Flow-based control offers more stable velocities across densities.

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.