Variable Speed Limit for Freeway Work Zone with Capacity Drop Using Discrete-Time Sliding Mode Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it