Traffic Modeling of a Railroad Crossing Adjacent to a T-Intersection Using Synchro 8
Bibliographic record
Abstract
This paper provides a study on the emulation of a railroad crossing using Synchro 8 at the Canadian Pacific Railway (CPR) crossing at Mile 63.12 in the Shuswap Subdivision in the City of Salmon Arm. This study is part of a safety and design improvements report addressing the safety issues raised by Transport Canada. Four design options were developed in this report with the traffic modeling results of each option being key criteria in evaluating the recommended option. In this paper, the railroad crossing was modeled by adding a dummy signalized intersection and modifying the signal phasing based on information collected from CPR and from site observations. The objective of modeling the railroad crossing was to emulate the vehicle queue lengths as they were observed on the site visit. In addition, origin-destination trips were accounted for in the traffic analysis to calibrate the model. The fine tuning of the analysis resulted in a successful traffic model emulation of the railroad crossing and traffic conditions as they were observed onsite. The fourth design option, which involves changing priority of the stop controlled minor-leg approach to a three-way stop, was chosen to be the recommended design based partially on the outcome of the traffic analysis. (A) For the covering abstract of this conference see ITRD record number 201309RT334E.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".