Variable Speed Limit for Freeway Work Zone with Capacity Drop Using Discrete-Time Sliding Mode Control
Bibliographic record
Abstract
Freeway work zone with lane closure has a direct negative impact on travel time, safety, and environmental sustainability. The capacity drop at the onset of the congestion can also further reduce the discharging rate at the work zone area and worsen traffic conditions. Existing studies have developed various variable speed limit (VSL) control methods to mitigate the congestion; however, a simple yet robust VSL control strategy that considers the nonlinearity induced by the capacity drop is still lacking. To address the above-mentioned issue, this study proposes a VSL strategy using a nonlinear traffic flow model and a discrete-time sliding mode control for freeway work zone. The developed traffic flow model incorporates the nonlinearity caused by the capacity drop at the work zone using the cell transmission model. The sliding mode controller is designed to drive the traffic state, which is acquired from the built traffic flow model, to the desired equilibrium state with different convergence rates. Under speed limit constraints, the VSL scheme is generated to regulate the traffic and mitigate the congestion. The proposed system is implemented and evaluated using the traffic microscopic simulator SUMO. The results indicate that the proposed VSL control can consistently improve the traffic mobility, safety, and environmental sustainability under the noisy traffic demand and different control scenarios. Compared with the uncontrolled scenario, the developed system shows improvement by approximately reducing 17% of the average travel time, 90% of the safety risk, and 6% of NOx, CO2 emissions, and fuel consumption.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".