Solving Various Train Approach Speeds to Highway Crossings Using Innovative Technologies
Bibliographic record
Abstract
The use of using cleaner energy (zero emissions) transportation has become a key focus in the North America even within rail transportation. Within in North America the migration from Diesel to Electric Locomotives, utilizing overhead catenary systems with voltages in the 25kV range for passenger trains has become the standard. In addition, “Shared-Use Rail Corridors” have become more prevalent in North America (USA and Canada), the use of Constant Warning Time Devices (CWTD) based on a change of inductance in the rail are less reliable within Electrified railroads. With shared use track, it is understood that a difference exists between freight and passenger train speeds, resulting in the need for other methods to detect and determine the correct approach times become a priority. Implementing Computer-Based Train Control (CBTC) systems or Positive Train Control (PTC) technology can mitigate the problem if they communicate / request highway crossing activation. But in locations where PTC is not being installed or in Canada where it is not required, other methods need to be explored. This paper will review and analyze the following: 1. Review the existing systems being deployed; 2. Evaluate the deployed systems effectiveness; 3. Test and record data using various innovative technologies including: Axle counters determining speed of approaching train, acceleration (+ / −); 4. Conclusion for the integrating new axle counter technologies and existing track circuits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".