Freeway work zone traffic state estimation with fault diagnosis
Bibliographic record
Abstract
Freeway work zone can cause disruption to local traffic and have adverse impacts on mobility and safety of road users and those who work in the work zone. To ensure an effective traffic management strategy, it is essential to accurately and instantaneously estimate the traffic states at the work zone area. While many traffic state estimation methods are proposed by previous studies, few of them consider the occurrence of freeway sensor faults, which may result in a large deviation in state estimation and potentially lead to an inappropriate traffic management strategy. To overcome the impacts of sensor faults and provide accurate traffic state estimation, this study presents an approach using sensor fault diagnosis for traffic state estimation at freeway work zone area. Considering the capacity drop, the switching mode model with Kalman filter was used to estimate the traffic states. With the analysis of the density residuals generated by traffic sensors and probes, the fault diagnosis can detect the type of sensor faults and reconfigure the estimation model. The proposed system is implemented and evaluated in traffic simulator SUMO under a realistic freeway work zone environment. The results show that the developed system can accurately identify the type of fault in short time. An accurate traffic state estimation is provided and fairly maintained under fault-free and sensor-fault scenarios respectively.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".