Mitigating Grade Crossing Blockage Queues by Modifying Signal Timing Plans: A Network-Level Microsimulation Approach Using Train Detection and Probe-Based Traffic Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it