Analysis of Path Distribution Characteristics and Safety Impact of Pedestrians Crossing in the Advance Right-Turn Lane
Bibliographic record
Abstract
Pedestrian crossing chaos has become one of the significant problems of urban traffic nowadays, and the irregular behavior of pedestrians affects traffic operation and regional safety to a certain extent. Aiming at the safety of pedestrian crossing in the advance right-turn lane, the pedestrian path deviation (PPD) is proposed to quantitatively describe the pedestrian crossing path deviation and analyze the impact of PPD on pedestrian crossing safety. With four advance right-turn lanes at an intersection in Qingdao as the investigation site, the spatiotemporal information of pedestrians and motor vehicles is collected to analyze the characteristics of pedestrian path distribution and the influencing factors of pedestrian crossing under the factors of traffic flow and environment. The pedestrian-vehicle conflict characteristics are analyzed from the perspective of conflict rate and deceleration-to-safety. The severity level of human-vehicle conflict is classified according to the cumulative frequency of safety deceleration, and the severity model of human-vehicle conflict based on ordered probit is established. The results show that buildings such as large shopping malls and transportation hubs have an attractive effect on pedestrians. Under the influence of unilateral buildings, the proportion of irregular use of pedestrian crosswalks reaches 46.9%. There is a significant positive correlation between pedestrian flow and PPD index, and the increase of pedestrian flow causes the aggravation of pedestrian path deviation. The results of the analysis of PPD with pedestrian-vehicle conflict rate and conflict severity show that the increase of PPD significantly increases the risk of pedestrian-vehicle conflict occurrence; in addition, motor vehicle speed and PPD have significant effects on the severity of pedestrian-vehicle conflict, and the increase of both vehicle traffic speed and PPD causes the aggravation of the severity level of pedestrian-vehicle conflict. Combined with the above findings, the PPD index has some value in quantifying pedestrian spatial violations as well as regional safety evaluation, and the findings can provide theoretical guidance for the establishment of traffic facilities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".