Efficiency and Reliability Analysis of Self-Adaptive Two-Stage Fuzzy Control System in Complex Traffic Environment
Bibliographic record
Abstract
In this paper, a self-adaptive, two-stage fuzzy controller is established to realize the real-time online optimization of traffic signal timing plan, which takes multimodels of transportation as the research object to analyze the reliability of the control system at the isolated urban intersection. In this system, the first stage calculates traffic urgency degree for all red phases and selects the red phase with maximum traffic urgency degree as the next green phase. The second stage determines whether to extend or terminate the current signal phase. Aiming at the problems of the parameters of membership functions empirical settings and insufficient response to the real-time fluctuation in traffic flow, the controller introduces an improved hybrid genetic algorithm to solve it and enable the controller to self-learn. Finally, a microsimulation platform is constructed based on the VISSIM and Python language to evaluate the efficiency and reliability of the controller under complex actual traffic conditions. Results showed that the average delay time per vehicle is reduced by 14.59%, while the average number of stops per vehicle is reduced by 0.71% compared with the traditional control method. Results indicate that the traffic signal timing plan generated by the controller can efficiently improve the intersection traffic capacity and has good efficiency and reliability under the condition of medium saturation and unsteady flow.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".