Impact of Road Pavement Condition on Vehicular Free Flow Speed, Vibration and In-Vehicle Noise
Bibliographic record
Abstract
Road infrastructure in good condition is a key requirement for efficient transportation systems which leads to economic prosperity and improved quality of life. However, road surface conditions deteriorate over time according to traffic loads and environmental factors. Poor road conditions lead to congestion, accidents, lost productivity, and driver fatigue. This work considers the relationship between road pavement condition and vehicle speed, vibration, and in-vehicle noise. A 7 km section of the Grand Trunk Road, Peshawar, Pakistan divided into 280 segments (140 for each lane), of length 50 m was observed and the Pavement Condition Index (PCI) of each segment was determined based on the recurrent distress type and density according to ASTM D6433-011 guidelines. The number of very good, satisfactory, fair, poor, and very poor conditions are 51, 52, 81, 48, and 42, respectively. The mobile app BotlnckDectr was employed to measure vehicle speed, RPM, noise, vibration, GPS location, and time. Statistical analysis was employed to determine the relationship between PCI and vehicle speed, vibration, and in-vehicle noise. The results obtained indicate that noise and vibration increase by 3.3% and more than 30%, respectively, as the pavement condition changes from good to very poor, and vehicle speed decreases by 8.8%.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".