Mitigating Grade Crossing Blockage Queues by Modifying Signal Timing Plans: A Network-Level Microsimulation Approach Using Train Detection and Probe-Based Traffic Data
Bibliographic record
Abstract
Road user delays arising from grade crossing blockages represent a major component of road user cost. Despite this recognition and recent advancements in data availability, monitoring technologies, and modeling tools, little work has been done to simulate operational impacts of crossing blockages at the network level. This article describes a proof-of-concept study that develops a traffic microsimulation model to quantify impacts of crossing blockages under various recovery signal timing plans. The study focuses on a signalized intersection near a grade crossing in Winnipeg, Canada. Considering various timing plans and blockage durations, the model reveals that the newly proposed signal timing plans shortened the queue clearance time relative to the plan currently used at the intersection. In doing so, the study demonstrated the value of integrating new traffic and crossing blockage data sources within a network-scale microsimulation model. Further work could be done to tailor the model to assess other types of operational and infrastructure solutions to problems associated with crossing blockages. Such work could help agencies select appropriate solutions and prioritize implementation.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".