Analysis of Pedestrian Lane Change Behavior Spectrum Based on Video Data at Ticket Gate Facilities in Subway Stations
Bibliographic record
Abstract
Using motion unit tracking technology, the pedestrian motion parameters were extracted from the monitoring video data at the ticket gate facilities in subway stations and the indicators of the lane change behavior were determined. The pedestrian lane change behavior spectrum of ticket gate facilities in subway stations was constructed from the three elements of types of ticket gate facilities, pedestrian flow, and lane change behavior, and the quartile method was used to determine the upper and lower thresholds of the indicators. The results showed that when the pedestrian flow was (5, 10] ped/min, the average values of displacement, distance, and cumulative side shift distance were the largest and the thresholds were the largest. When the pedestrian flow was (10, 15] ped/min, pedestrians generally adopted a faster walking speed to change lanes. With the increase of the pedestrian flow, the average value of the change in direction of the movement increased and the longitudinal distance at the gate-type ticket gate facilities was greatly affected by the pedestrian flow. The number of lane changes was generally once. The research results can provide a basis for scientifically setting up ticket gate facilities and reducing congestion risks caused by abnormal lane change behavior.
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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 | 0.003 | 0.002 |
| 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".