Comparative analysis of probe‐based ramp metering with detector‐based and pretimed ramp metering
Bibliographic record
Abstract
SUMMARY In this paper, a probe‐based adaptive ramp metering approach has been developed and compared with a detector‐based and pretimed ramp metering approaches using PARAMICS microsimulation model made by Quadstone Limited. Both examined probe‐based and detector‐based ramp metering approaches adopt the same problem formulation; however, they differ by the data source that provides estimates of their input parameters. The probe‐based approach takes as main input space mean speed extracted from vehicle probes moving constantly on the entire freeway. The detector‐based approach takes the density readings directly from point detectors placed upstream and downstream of each on‐ramp. The developed approach formulates the ramp metering rates to simultaneously reduce freeway travel time and decrease delays on ramps. A dynamic weighting scheme is adopted in this formulation to give different weighting schemes to the various freeway sections and the on‐ramps. These weights are able to adjust dynamically based on prevailing and forecasted traffic conditions to give priority in each step to the sections of the freeways or/and on‐ramps experiencing critical traffic conditions. The comparative analysis of the three ramp metering approaches was conducted on an 8‐km freeway section on Highway 2 in Calgary, Alberta, Canada. The results of the analysis indicate that the probe‐based algorithm consistently outperformed the two other approaches in terms of all performance measures. The probe‐based approach is shown to exceed the performance of the detector‐based approach with a market share of vehicle probes as low as 3%. The results of the analysis are promising and show the effectiveness of developing ramp metering control algorithms taking the main input parameters from vehicle probes on the freeway. Copyright © 2012 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".