Improving Walkability of People with Reduced Mobility at an Intersection Using Microsimulation In VISSIM And SSAM
Bibliographic record
Abstract
In this research project, the evaluation of the accessibility and mobility difficulties faced by people with disabilities when circulating around a vehicular intersection is addressed.To carry out this evaluation, the Vissim and SSAM programs were used.The assessment of conflicts is carried out through the analysis of heat maps obtained with SSAM.In the context of this article, an analysis is carried out of the number of conflicts between vehicles on a road segment that is part of a traffic signalized intersection, characterized by the constant presence of congestion.Along this stretch, a decrease in lanes, variability in lane widths and lack of uniformity are observed, generating a point of congestion that gives rise to various types of vehicular conflicts.To carry out this evaluation, the micro simulation software VISSIM 8.0 was used, which models the real situation of the intersection.Two new scenarios are proposed, in which suggestions for geometric redesign are introduced in the infrastructure of the road section with the objective of analyzing the influence on the variability of the number of vehicular conflicts.The proposed methodology consists of three fundamental stages: (i) obtaining the passage times of four types of vehicles that circulate in the area and the passage times of people with disabilities along the track, (ii) the modeling in the Vissim program and (iii) the implementation of the proposed improvements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".