Operating Speed Prediction Models by Vehicle Type on Two-Lane Rural Highways in Indian Hilly Terrains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study aims to develop vehicle type-wise operating-speed prediction (OSP) models for heterogeneous traffic on two-lane rural highways in Indian hilly terrains. For the present study, 27 curves with varying geometric characteristics located along the National Highway (NH-953) connecting Netrang and Rajpipla in the western state of Gujarat, India, were selected. Speed data were collected using radar guns at three curve locations (entry point, midpoint, and exit point) in each travel direction for three dominant types of vehicles: motorized two-wheelers (2W), cars, and heavy commercial vehicles (HCVs). OSP models were developed for different vehicle types at three curve points using the backward elimination stepwise regression (BSR) technique. The results revealed that the preceding curve point’s operating speed, curve length, and tangent length positively affected operating speed. In contrast, deflection angle, curve sharpness, and grade had adverse effects. The curve geometric characteristics had the most negligible impact on the operating speed of 2W and a significant effect on HCV. Among all the curve-related aspects, curve length was the most significant variable and affected the speed of all three vehicle types, followed by curve sharpness. Further, the developed OSP models were applied to the other hilly terrain to check the transferability of the model. As an important outcome, the developed OSP models were used to evaluate geometric design consistency. This highlights the need for geometric and traffic-calming measures to improve highway operating-speed consistency and driver safety.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it