Decentralized Coordinated Optimal Ramp Metering: Application to the Gardiner Expressway in Downtown Toronto
Bibliographic record
Abstract
Dynamic traffic control measures provide a set of cost effective congestion mitigation solutions for the escalating congestion problems of metropolitan areas, among which ramp metering (RM) is an effective approach. While independently controlling on-ramps can effectively prevent freeway breakdown, this may sacrifice the users of the on-ramp for the benefit of others. Additionally, limited queue capacity of on-ramps will limit RM effectiveness. In this paper, the authors present a coordinated and decentralized freeway ramp metering algorithm by metering the rates of adjacent on-ramps to efficiently utilize the queue space available on all on-ramps along freeways. The controllers are designed based on multi-agent reinforcement learning, to obtain metering rates without the need for a mathematical model of the freeway. The agents utilize a function approximation method to overcome the curse of dimensionality issue associated with conventional reinforcement learning approaches. The proposed RM control system is applied to a calibrated microsimulation model of the Gardiner Expressway in Toronto, Canada. The Gardiner expressway is the main freeway running through Downtown Toronto and suffers from extended periods of congestion. The proposed coordinated RM algorithm when applied to the Gardiner model resulted in 50% reduction in total travel time compared with the base case scenario and significantly outperformed approaches based on the well-known ALINEA RM algorithm. This improvement was attained without compromising the permissible on-ramps maximum queue limit.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".