Advanced Transition Preemption Strategy for Signalized Intersections near Highway-Rail Grade Crossings with Dual Tracks
Bibliographic record
Abstract
The current preemption method for traffic signals at intersections located near highway-rail grade crossings (IHRGC) is known as the standard preemption (SP). The SP strategy is designed to give priority to the phases which clear vehicles off the railroad tracks as quickly as possible before a train arrives at HRGCs and provides the drivers and pedestrians with a minimum warning time (MWT). However, the SP considers neither pedestrian safety nor system efficiency at IHRGCs. As a result, this may lead to safety and delay problems at IHRGCs. To solve the problems, a state-of-the-art transition preemption strategy (TPS) algorithm, named TPS_DT, is developed in this paper. The new TPS algorithm is designed for corridors with multiple HGRCs that have dual tracks. An urban highway corridor with multiple HRGCs in Lincoln, NE, was selected as the study corridor. A calibrated VISSIM model of the study corridor was used to test the safety and efficiency of the proposed algorithm. The algorithm was coded in VAP, which is an add-on module of VISSIM. A roadway-railway corridor with multiple IHRGC and dual rail tracks in Lincoln, NE, was used as the testbed. The Measurements of Effectiveness (MOEs) used for evaluation include the rate of pedestrian phase cutoffs, intersection vehicle delay, and corridor vehicle delay. It was found that TPS_DT can significantly improve pedestrian safety and reduce vehicle delay at IHRGCs. Furthermore, the effects of train arrival prediction errors on safety and efficiency of the IHRGCs are also analyzed in the paper.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".