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Record W2594090028 · doi:10.1061/jtepbs.0000047

Improved Pedestrian Sight-Distance Needs at Railroad-Highway Grade Crossings

2017· article· en· W2594090028 on OpenAlexafffund
Said M. Easa, Xiaobo Qu, Essam Dabbour

Bibliographic record

VenueJournal of Transportation Engineering Part A Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPedestrianIntersection (aeronautics)SightPedestrian crossingGeometric designTransport engineeringTangentLevel crossingComputer scienceSimulationEngineeringMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

This paper presents an improved model for pedestrian crossing time that is used to establish preliminary guidelines for lateral clearance needs on railroad-highway grade crossings. The improved model includes new elements that are lacking in existing models such as pedestrian observation-reaction time, length of pedestrian unit, and safety margin. A general model for the required lateral clearance, that is applicable to any number of tracks on crossings located on horizontal curves or on tangent sections, was developed. The model can be used to determine the required lateral clearance to the right and to the left of the crossing, including maximum lateral clearance, its location, and lateral clearance at a specified location. A comparison of pedestrian crossing sight distance with intersection (vehicle) sight distance shows that lateral clearance needs for pedestrians are not generally satisfied by those currently available for vehicles. The presented design guidelines promote pedestrian safety and should be of interest to highway and railroad professionals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.015
GPT teacher head0.227
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2017
Admission routes2
Has abstractyes

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