Predicting the level of use of underground routes in a multi-level urban environment
Bibliographic record
Abstract
Movement in dedicated pedestrian networks in urban environments is an important area for study in the field of urban planning. Researchers have investigated various related factors in path choice with regard to these settings. However, the preference of pedestrians for underground and surface routes in a multi-level urban system is still relatively unknown, making it difficult to model pedestrian dynamics in such complex spatial systems. The purpose of this thesis project is firstly to investigate the factors affecting pedestrian path choice in a multi-level urban environment and secondly, to propose an assignment model for pedestrian circulation in a multi-level system, with parameters from the first study. The results obtained from three operating tunnels in downtown Montreal show that seasonal change has an effect on pedestrian preference for path choices, suggesting that weather conditions are a major factor related to the use of underground routes. Although there is no statistically significant relationship between personal or systematic factors and preference for routes, the influence of systematic factors can be observed in the three cases. The assignment model is applied in a case study of Concordia tunnel system under construction. This study contributes to the investigation of factors affecting path choices, surface or underground routes, and proposes a new model to project pedestrian flow.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".