A Citywide Location-Allocation Framework for Driver Feedback Signs: Optimizing Safety and Coverage of Vulnerable Road Users
Bibliographic record
Abstract
Driver feedback signs (DFSs) are being adopted increasingly by municipalities around the world, as they have been proven to be a cost-effective countermeasure that improves road safety. However, research is still needed on developing a location-allocation framework to determine the optimal implementation strategies for DFS placement. Hence, the main aim of this paper is to formulate a location-allocation optimization problem with the objective of reducing vehicular collisions (ΔC) while enhancing spatial coverage for vulnerable road users and facilities (Cov). Two distinct planning scenarios, namely, an all-new and expansion scenario, were proposed in the framework. It was found that ΔC and Cov can be improved by up to 149.44% and 69.27%, respectively, in the all-new scenario. Two expansion scenarios were done with 10 and 20 additional units into the system. It was found that ΔC can be improved by up to 30.22% and 51.61% for the additional 10 and 20 DFSs, respectively. Likewise, the Cov can be improved by up to 14.64% and 29.27%, respectively. This framework provides decision makers with the freedom to simulate and optimize their DFS network by balancing the needs of the road users, vulnerable facilities, and traffic safety in locating DFSs over an urban road network.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".