USING A LATENT-CLASS MODEL TO EXAMINE SENSITIVITY OF LOCAL CONDITIONS ON STAIR AND ESCALATOR CHOICE IN TORONTO SUBWAY STATIONS
Bibliographic record
Abstract
Understanding the rationale behind routing decisions for pedestrians in complex spaces remains a technical challenge for space design and crowd management. The ability to correctly predict crowd usage of passages, open spaces and vertical transport (e.g. stairs, escalators) would allow for better design and operation of such facilities. To date, minimal research has been performed examining vertical transport choice in the context of pedestrian path finding, and existing models have been static in nature. This study aimed to better understand the relative weighting pedestrians give to an initial predisposition versus that based on more dynamic conditions (e.g. queuing) at the entrance of the facility. Two types of discrete choice models were estimated, a standard binomial logit, which assumes a homogeneous population, and a latent-class model, which allows for population segmentation. The latter was formulated to consider the process as a combined decision based on information available before and after reaching the facility. A strong aversion to stair use was found, with an additional dampening effect of increasing opposing flow. Conversely, approaching from the staircase side, having preceding pedestrians take the stairs and higher flows or escalator queues were found to increase the chance of stair use. The latent-class model also elucidated a relatively minor influence of dynamic conditions on overall choice. Significant predictive differences were not found between the models; however, the ability of the latent class model to identify the relative weight of local dynamic conditions allows for insight into the decision process of pedestrians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".