Using Virtual Reality Simulation for Optimizing Traffic Modes Toward Service Level Enhancements.
Bibliographic record
Abstract
Using Virtual Reality Simulation for Optimizing Traffic Modes Toward Service Level Enhancements. Firas Habbal, Fawaz Habbal, Abdallah Al Shawabkeh, Abdulla Al Nuaimi and Ammar Safi Pages 831-837 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Traffic congestion and roads service level are major issue in many countries. Using technology such as simulation offers effective approach to better understanding the problem, and predict optimal solutions. This paper examines the application of Virtual reality (VR) for evaluating the roads service level of five different scenarios in UAE as new method to evaluate and enhance road service levels, as well as understanding the potential risks and costs for applying those scenarios into reality. The study will test the usefulness of VR simulation to enhance traffic service level, second creation of traffic objects to explore potential usage, third understand the interaction between users and digital objects. All hypothesis have significant impacts toward enhancing service level, and the overall findings are consistent and clear. The level of technological orientation was examined to the overall implementation. The results help understanding key issues and potential service level in development of future VR applications in roads construction. Keywords: Virtual realities; automation in Construction; roads service level; traffic objects enhancements.; DOI: https://doi.org/10.22260/ISARC2019/0112 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".