Design of Light-Rail Transit Signal Priority to Improve Arterial Traffic Mobility
Bibliographic record
Abstract
Transit Signal Priority (TSP) is a cost effective strategy for improving the movement of transit vehicles, such as Light Rail Transit (LRT), buses or streetcars, through controlled intersections. Application of TSP strategies improves the reliability and quality of services of transit vehicles, while causes less disruption to normal traffic. City of Edmonton in Canada has recently extended the LRT system, most of the part of which run through at grade intersections. LRT is currently operating under preemption which is causing significant delay to other traffic. The problem is more pronounced during peak hours when the frequency of LRT is increased to 5 minutes. This has led to dissatisfaction among the motorists along the corridor. In this paper, different TSP strategies for improving the performance of the LRT corridor are analyzed. VISSIM, a micro-simulation tool with its Ring Barrier Controller (RBC) emulator is used to implement the strategies at a major intersection during peak hours. Field data for both AM and PM peak hours were collected at four intersections along the corridor for the calibration of the VISSIM model. The three TSP strategies explored in this paper are (a) LRT preemption (b) LRT prediction, and (c) LRT prediction together with transit bus priority. Each strategy is evaluated in terms of a number of measures of performances. It is found from the results that the strategy (b), where LRT arrival time is predicted to provide LRT preemption, yields the highest improvement in the corridor performance.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".