A Two‐Stage Multiobjective Optimization Approach for Expressway Guide Sign Information Selection

The selection of information for expressway guide signs requires a consideration of multiple factors, including geographical location, traffic demand, network cost, and information relevance. As expressway networks continue to expand and their topologies become increasingly complex, relying solely on expert experience for guide sign information selection has become insufficient. To address this issue, a novel two‐stage multiobjective optimization framework is proposed. In the first stage, a route‐swapping algorithm is employed to solve a transformed system optimization equilibrium model to generate the link flow distribution. This distribution then serves as parameter input for the second stage, where a multiobjective mixed integer linear programming (MILP) model is formulated to decide guide sign information. We consider three objectives of maximizing consistency between guidance information and traffic flow distribution, maximizing continuity of guidance information, and minimizing driver’s recognition time. The Pareto optimal solution set of the multiobjective model is identified through parametric search, and the TOPSIS method is applied to determine the global optimal solution, generating the guide sign information scheme for newly constructed expressway segments. Validation on the case‐study expressway shows that the proposed framework provides a scheme that satisfies all hard constraints while exhibiting high consistency with geographical and traffic flow distribution patterns, strong information coherence, and effective control of redundant information. This study presents an efficient, standardized methodology for optimizing expressway guide sign information selection.