A Novel Virtual Reality Traffic Simulation for Enhanced Traffic Safety Assessment
Bibliographic record
Abstract
Transportation safety studies identify and analyze different contributing factors affecting the safety of road users using virtual reality (VR) traffic simulations in game engines (e.g., Unity). They often either use simplified VR traffic simulation or develop a more advanced simulation requiring substantial technical expertise and resources. The Simulation of Urban Mobility (SUMO) software is widely employed in the field, offering extensive traffic simulation rules such as car-following models, lane changing models, and right-of-way rules. In this study, we develop a novel virtual reality traffic simulation by integrating two different simulation software, SUMO and Unity, and developing a virtual reality traffic simulation where a VR user in Unity interacts with traffic generated by SUMO. In our methodology, we first explain the process of creating road networks. Next, we programmatically integrate SUMO and Unity. Finally, we measure how well this system works using two indicators: the real-time factor (RTF) and frames per second (FPS). RTF compares SUMO’s simulation time to Unity’s simulation time each second, while FPS counts how many images Unity draws each second. Our results showed that our proposed VR traffic simulation can create a realistic traffic environment generated by SUMO under various traffic densities.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".