Evaluation of Complex At-Grade Rail Crossing Designs Using a Driver Simulation
Bibliographic record
Abstract
This paper discusses a unique and first time application of a driver simulator to assess a complex at-grade rail crossing design in Canada. Based on recommendations from a road safety oriented peer review of a proposed upgrade design of a railway grade crossing in the City of Ottawa, the objective of this simulation was to optimize the positive guidance offered to drivers using the crossing and associated roadway elements, by designing a more understandable, visible and driver oriented approach environment. The first section of this paper provides an overview of the existing grade crossing environment and the challenges created by planned road network and development changes in the vicinity of the crossing. The technical complexity and features associated with the proposed design solution that lead to the decision to use a driver-simulator approach to the evaluation of positive guidance and warning systems at this grade crossing are also discussed. In the second section the authors discuss the data and information requirements necessary to construct the computer model for insertion into the driver simulator – and the particular challenges encountered in meeting these needs. The last section of the paper details the specifics of the simulator study design, with a specific focus on the human factors methodology and considerations used to evaluate the performance of drivers within the proposed crossing environment. Observational driver behavior and eye movement measures collected during participants’ drives are examined and key findings of the study are also summarized in the paper. The report concludes with a discussion of the advantages, limitations, and challenges associated with using a driver simulator approach to evaluating real-world application scenarios.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".