An Implementation of the AI-based Traffic Flow Prediction in The Resilience Control Scheme
Bibliographic record
Abstract
Today, often a reliable and dynamic sensor system is found to be necessary to control intelligent transportation systems. While these dynamical sensor systems are often found to be useful for the ordinary situations, the resilience-control-related issues are not yet fully addressed in the literature. The traffic flow is an important resource, which if found to be disturbed by a malicious threat it may cause further insecurities, e.g. if the sensor data is not accessible due to a malicious sabotage of the on-the-road sensors. Furthermore, often centers for the data gathering and prediction are suffering from data-loss because of imperfections of the data gathering itself. To overcome the resulting difficulties, a prediction engine is required to estimate the traffic flow, with the ability to compensate for the lost sensors. In this paper, a traffic flow prediction engine is proposed in which the artificial-intelligence-based methods are used to perform the optimization task. This method is implemented for the test in the real-world situation and its efficiency in traffic estimation is proved to be reliable. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained with the particle swarm optimization (PSO) algorithm and the Artificial Neural Network model (ANN) is used to predict the flow. In addition, The Principal Components Analysis (PCA) method is adopted to reduce the dimension of the features. The results show the method's efficiency in predicting the traffic flow. This prediction engine can be practically implemented and used as a replacement for the sensors to predict the traffic flow.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".