Development of Public Transit Measures to Mitigate the Impact of COVID-19 on Pedestrians and Station Performance using PTV Vissim Simulation
Bibliographic record
Abstract
In this study we explored the possible changes in passenger behavior on transit stations due to disruptions such as COVID-19 and the impact on station performance. A trade-off is observed between the reduced risk of virus transmission through increased physical distancing and subsequent negative impact on a station's performance. To evaluate this trade-of, a simulation model of Marlborough station in Calgary, Canada was developed using PTV Vissim. Passenger behavioral changes were implemented by manipulating the Social Force Model (SFM) parameters within the simulation model. The impact from these changes were measured by the developed model and was simultaneously validated with the theoretical expectations derived from equations on the SFM parameters. Alternative station designs were simulated and tested to allow separated flow of passengers in different parts of station such as pedestrian bridges and stairways. The results from the study found that pedestrian physical distancing had a profound negative impact on the transit station's performance. However, these effects can be addressed through simple low-cost station modifications. Ultimately, the results of this study can be used as a reference for transit authorities to develop mitigation strategies against possible resurgences of COVID-19 or other infectious diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".