Online Dynamic Pricing of HOT Lanes Based on Corridor Simulation of Short-Term Future Traffic Conditions
Bibliographic record
Abstract
A flexible simulation framework was designed for the Ministry of Transportation of Ontario to facilitate the evaluation of High-Occupancy/Toll (HOT) lanes. This framework implements dynamic HOT lane pricing based on the simulation of short-term future traffic conditions in both the HOT lane and the adjacent general-purpose lanes. The pricing algorithm attempts to simultaneously maximize utilization of the HOT lane and maintain a specified minimum average speed. The framework is oriented to on-line operation although self-learning functionality maintains a pricing “look-up” table for back-up or standalone use. Dual, interconnected simulation instances in the AIMSUN environment maintain a reference traffic state and evaluate alternative toll-rate strategies for the next time interval. Critical elements of the framework, including the simulation environment and the pricing algorithm, were implemented and tested in the context of a hypothetical conversion of existing High-Occupancy Vehicle (HOV) lanes to HOT lanes in three corridors in the Greater Toronto Area. Results observed for this initial implementation of the framework were encouraging, given a constrained and complex environment, although there is headroom for further enhancement.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".