Macroscopic and microscopic traffic parameter models for operating speed on horizontal highway curve / Tuan Badrol Hisham Tuan Besar
Bibliographic record
Abstract
Operating speed is known by the maximum speed of the road users operate their vehicles along a travel path under favorable traffic and weather on the horizontal curve of a two-lane rural highway under free flow conditions, the 85th percentile distribution has been observed to be the most frequently used method to measure the operating speed associated with the geometric features. Meanwhile, the design speed includes logical consideration reflecting the topography, function of all class highway and operating speed. Locally, until the recent release of guideline by the authorities, the Public Work Department in 2015 has not embedded the Operating Speed Model in their document. With the existence of the model, it will enable the designer to anticipate the speed of vehicles travelling along a distance. The speed prediction is considered essential because it can produce an estimated speed in handling the vehicle. This will assist in reducing accident risks at curves, 85th percentile speed prediction model is needed for the Malaysian scenario to ensure the speed design relevancy. The study aims to establish an Operations Speed model based on local environment, targeting at the curve section. This will also take into consideration the geometric factor, traffic factor and road roughness. As for methodology, several tools were applied for data collection across Malaysia namely Laser Gun, Video VBOX, Ball Bank, Roughnometer and Automatic Traffic Classifier. The captured data can be grouped as geometric data, traffic data and comfort data. The Multiple Liner Regression was applied in constructing the Operating Speed Prediction Model. The study was conducted on 9 sites throughout Malaysia. Each site has different site parameter of geometric and traffic composition. Completion of this study has resulted in three significant models being developed namely VD85CS, V85CM and V85CE. To confirm the usefulness of the models in performing the prediction of the operating speed, the developed models were then further validated by performing t-test, RMSE, MAPE and MAE by comparing the developed models with empirical data, and also further compared with existing models from Transportation Association Canada, Federal Highway Association and Institution of Engineers Malaysia. The developed models were test for its sensitivity to identify the effect of changes on each individual variable for the developed models. The comparison shows that the developed models are more superior for local traffic environment. Therefore, the developed models in this study are proposed to be a starting basis of the 85th percentile speed model to be implemented in the Malaysian geometric road design guidelines. The guidelines are to be issued by the Public Work Department of Malaysia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".