Maximizing toll revenue and level of service on managed lanes with a dynamic feedback-control toll pricing strategy
Bibliographic record
Abstract
In recent years, congestion pricing emerged as a cost-effective and efficient strategy to mitigate congestion on freeways. This study develops a dynamic toll pricing strategy based on feedback control rules and compares its performance with the current strategy deployed on the I-95 express lanes in south Florida. The proposed strategy aims to maximize the toll revenue while maintaining a minimum desirable level of service on the managed lanes. A detailed numerical example is provided to demonstrate how the proposed strategy works and the performance is examined for low and high traffic demand. An external module is developed to execute the strategy in real time during VISSIM runtime. The impact of the value of time based on the income level is also examined. Three values in the range of 60% to 120% of the mean hourly income are used. The results show that for high demand, an increase in the probability of choosing managed lanes becomes more evident, with the highest increase observed for the case of 120%. Also, during high traffic demand, high income groups exhibit higher probabilities of choosing the managed lanes despite the increase in toll rate due to the increase in travel time savings. When compared to the currently adopted toll pricing strategy on I-95, the proposed strategy shows a steadier toll rate profile and a greater overall toll revenue, while maintaining the speed at nearly 72.4 kph (45 mph).
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".