Investigation of Pedestrian Movement in Groups and in High-Density Bottlenecks Using Discrete Choice Modelling Framework
Bibliographic record
Abstract
Walking as a non-motorized mode of transport is an essential component of sustainable environment.To increase the share of walking in transportation and accordingly to plan and manage pedestrian areas, a deep understanding of pedestrian movement behaviour is required.Developing a model that can reproduce pedestrian behaviour can be used as a tool for assessing the existing and designing new pedestrian spaces.The aim of this thesis is to provide walking behaviour models based on discrete choice framework first to study the behaviour of pedestrians walking in groups and second to investigate pedestrian movements at bottlenecks under high density situations.Discrete choice models focus directly on behavioural aspects of pedestrian movement and model the choice behaviour of individuals when they have to select among a set of alternatives.The correlation between alternatives is captured by Cross-nested logit model in this thesis.Data has been extracted from real-world video recordings of pedestrian crowds in order to calibrate and validate walking behaviour models.In the first part of this thesis, group behaviour has been modeled based on the tendency of group members to maintain group unity.Maximum likelihood estimation has been used for the calibration purpose.The significance of social bonds between group members in presenting a realistic walking behaviour has been revealed in the estimation process.In the second part of the thesis, a simulation tool has been developed to calibrate and validate a model presenting pedestrian behaviour at high density bottlenecks.The validation results confirm the acceptable performance of walking models proposed in both parts of the thesis.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".