Human-Vehicle Intersection Traffic Lights Timing Optimization Research
Bibliographic record
Abstract
The purpose of this research is to find a traffic light timing optimization scheme. During the research, an intersection between Xi’an Mingguang road and the Fourth FengCheng road was chosen to analyze the crossing time distribution of pedestrians who were separated from west-to-the-right-turn vehicles during which the method of breaking off both ends of pedestrian green light signals was used. The VISSIM software was used for traffic simulation, aimed at improving traffic volume and right-turning vehicle average speed for less vehicle queuing delays, less human-vehicle conflicts, and better security for pedestrians without excessive interruption on their street crossing efficiency. The optimal scheme is obtained and the result shows that (1) the number of passing vehicles remains unchanged, with the queuing delay reduced by 5.78% and crosswalk passing speed increased by 19.01% compared with the original one. (2) As the scheme effect is positively correlated with the increase of right turn vehicle numbers, the scheme could be adopted for urban traffic management based on the local situation, which is not only in peak traffic hours but also in the flat peak time to ensure vehicle efficiency and pedestrian safety in the light of “vehicle yielding to pedestrians” regulation. (3) The scheme could also be adopted in cities with no “vehicle yielding to pedestrians” policy for both people-vehicle separation and pedestrian safety when crossing streets.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".