2023/08/28 by K. Kinjo, Kinjo, Kayo, Akiyasu Tomoeda +1
Engineering · Social Sciences · #Cellular Automata and Lattice Gases (nlin.CG) #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Statistical Mechanics (cond-mat.stat-mech) #Traffic Prediction and Management Techniques #Traffic control and management #Transportation Planning and Optimization
paper · pdf · doi:10.48550/arxiv.2308.14291
openalex publication_date 2023/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.