Enhancing Performance of Simulation Models for Rail Transit Dwell Time Considering Passenger Flow Modeling
Bibliographic record
Abstract
Dwell time is an important part of the total travel time in urban rail transit which directly impacts the system's reliability and the line capacity. Advancements in microsimulation software packages allow us to develop a detailed model of rail transit stations and test various strategies and their efficiency to improve the dwelling process in urban rail operations. In this study, we developed dwell time models in PTV-Vissim and then, proposed a method to improve simulation results by including passengers’ walking behaviour and their interaction within the transit system. The Vissim's user-friendly graphical interface was used to develop the dwell time models and afterwards, the Vissim models were improved by accessing Vissim objects through the COM interface in Python to enhance the simulation performance. The results show that the basic dwell time model in Vissim can be improved and capture more realistic situations after considering passenger walking behaviours.
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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 | 0.000 |
| 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".