Fault-Tolerant Control of Variable Speed Limits for Freeway Work Zone With Recurrent Sensor Faults
Bibliographic record
Abstract
Congestion associated with freeway work zones can adversely affect mobility, safety, and sustainability. Variable speed limits (VSL) control has been widely studied to mitigate the congestion caused by lane closures at work zones. However, most VSL controllers are designed without consideration of traffic sensor faults, especially recurrent sensor faults (RSFs) that commonly exist in freeway transportation systems. Therefore, this study proposes an interacting multiple model approach with a pseudo-model set (IMMP) to achieve VSL control with fault tolerance to different types of RSFs. With the design of a traffic flow model, an adaptive model set is developed using likelihood estimation to reduce the associated computational complexity. To ensure reliable RSF diagnosis, state covariance adaption is proposed to compensate for potential discrepancies caused by improper model parameters. A pseudo-mode set is designed to provide accurate traffic state estimations for VSL control without the prerequisite of a good match between the model parameters and the extent of corresponding sensor failures. The proposed system is evaluated under a realistic work zone environment using the traffic simulator SUMO. The results demonstrate that the system can achieve reliable RSF diagnosis and consistent improvements in mobility, safety, and sustainability near a freeway work zone area despite RSFs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".