Automatic Optimization of Guidance Guardrail Layout Based on Multi-Objective Evolutionary Algorithm

Abstract

Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places. However, existing methods of the guardrail layout mainly rely on manual design or mathematical models, which are not flexible or effective enough for crowd control in large public places. To address this limitation, this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary algorithm. The paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail layout. In addition to optimizing pedestrian passage efficiency and safety, the paper also considers the situation of pedestrian counterflow, making the guardrail layout more practical. Experimental results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion, as well as its significant improvements in passage efficiency and prevention of pedestrian counterflow.