A Two‐Stage Multiobjective Optimization Approach for Expressway Guide Sign Information Selection
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".