Study of criteria for assessing the level of vehicle traffic convenience on the streets
Bibliographic record
Abstract
Introduction. The assessment of street and road networks (SRN) traditionally relies on Level of Service (LOS) and Quality of Service (QOS) criteria, which often overlook user perception. This study addresses this gap by evaluating how drivers perceive traffic convenience, focusing on urban SRNs in Russia. Purpose of the research. The aim was to identify qualitative criteria for assessing vehicle traffic convenience on SRNs, incorporating driver perceptions into existing LOS methodologies. Materials and methods. Field studies were conducted using a mobile laboratory to record video footage of 21 road segments during peak hours. Fifteen male and six female drivers of varying ages evaluated these segments on a six-point scale based on 13 criteria, including safety, traffic density, and delays. Data from GPS/GLONASS trackers synchronized with video recordings were analyzed to determine LOS and QOS. Results. The study revealed discrepancies between driver perceptions and normative LOS values. Only 20–23% of cases aligned, with 50% differing by one level. Drivers rated conditions between B and D, avoiding extremes like E or F. Key criteria influencing perceptions included sudden appearances of motorcycles, heavy vehicles, and pedestrian proximity. Discussion. The findings highlight cultural and behavioral differences between Russian drivers and Western norms embedded in current LOS criteria. Drivers’ tolerance for conditions previously deemed unacceptable suggests evolving perceptions of traffic convenience. Conclusion. Existing LOS criteria, largely borrowed from U.S. and Canadian standards, require adaptation to local driving cultures and urban conditions. Integrating user perception into LOS assessments is essential for accurate evaluations. Resume. This study underscores the need for localized LOS criteria, combining traditional metrics with driver feedback to reflect real-world conditions. Suggestions for practical applications and directions for future research. 1. Develop a methodological document incorporating user perception into LOS assessments. 2. Expand studies to include diverse urban sizes and driving conditions. 3. Investigate long-term trends in driver perception to adapt criteria dynamically.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".