Examining Driver Behaviour at Freeway Ramp Terminals Based on Trajectory Data Collected Using Unmanned Aerial Vehicles and Video Image Processing
Bibliographic record
Abstract
This research utilizes Unmanned Aerial Vehicles (UAVs) to examine real-life speeds and behaviour of drivers at freeway entrance and exit ramp terminals.To achieve this goal, a total of 1,127 minutes of high-resolution aerial video data were collected for traffic movements at thirteen single-lane ramp terminals (seven entrances and six exits) using single and multiple UAVs.A complete space-time trajectory was extracted for each vehicle as it moved on the freeway right lane (FRL) or speed-change lane (SCL) and ramp using a combination of computer vision and deep learning tools.The trajectories were processed to extract relevant driver-vehicle behaviour measures (e.g., merging/diverging location, merging/diverging speed, acceleration/deceleration distances, SCL utilization rates, and accepted merging gaps).A descriptive analysis was performed for better understanding of driver behaviour over the entire stretch of the freeway ramp terminal segment, including FRL, SCL, and ramp.The trends of driver behaviour measures and their relationships with the SCL and ramp geometric characteristics were investigated under free-flow and platoon conditions.Results of the descriptive analysis highlighted differences between taper and parallel SCLs in terms of merging/diverging location, merging/diverging speed, and SCL utilization.Observations of data also confirmed the importance of accounting for the effects of ramp controlling features on the behaviour and vehicle acceleration needs, especially at exit ramps.Several statistical models were developed using regression analysis to model drivers' behaviour measures on SCLs and ramps.Moreover, a set of the observed merging accepted gap data were fitted to the models proposed in the literature to check which models provide the best fit.Results revealed that the models developed using trip data from SHRP-2 Naturalistic Driving Study (NDS) database relatively fitted the data better than other models in the literature.This finding is significant in validating the transferability of models developed using the NDS to other study areas iii in North America.Finally, the research concluded with a demonstration of the practical application of the developed regression models in reliability analysis, considering actual drivers' behaviour and speeds on SCLs and ramps.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".