Analysis of the Interaction between Passengers and Buses at a Congested Bus Stop through Simulation to Reduce Congestion Rate
Bibliographic record
Abstract
This article addresses the issue of user congestion at a high-demand public transportation stop in Lima, caused by prolonged waiting times and the perceived low quality of service. Micro simulation was conducted using VISSIM software to model ideal scenarios based on empirical data. The analysis considered key indicators such as service demand, congestion levels, and operational frequency of transportation lines. The study simulated the anticipated behavior of users who, upon having access to real-time bus arrival information, arrive at the stop just in time, thereby reducing waiting times. The results showed a 12.22% reduction in user congestion during peak hours and a more uniform redistribution of service demand during the same period. This optimization improved passenger flow and user experience without the need to alter the current bus frequencies, validating the economic and operational feasibility of the proposal. The research highlights the use of micro-simulation as an effective tool for designing sustainable solutions in urban environments, contributing to improved mobility and perceived reliability of public transportation.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".