Driver Behavior Performance at Freeway Exit Ramp Terminals: Investigation and Modeling
Bibliographic record
Abstract
Understanding how drivers exit freeways and interact with deceleration speed-change lanes (SCL) and ramp controlling features is important for adequate design of exit ramp terminals that meet drivers’ expectations and needs. Therefore, this paper investigates drivers’ diverging behavior along exit ramp terminal segments, including freeway right lane (FRL), SCL, and ramps based on video-based trajectory data collected using unmanned aerial vehicles (UAVs). Trajectories of 3,259 vehicles were collected at six sites as the vehicles moved on the FRL or SCL and off-ramp. Diverging behavior measures, including diverging speed, diverging location, deceleration rate on SCL, SCL utilization, and speeds at ramp gore nose and end of the SCL, were then extracted and used for the qualitative and quantitative analysis. The qualitative analysis showed that drivers exiting a freeway at taper-type SCLs tended to start deceleration on the FRL, which impacted the speeds of nonexiting vehicles. On the other hand, this behavior was not evident at parallel-type SCLs. Additionally, drivers were found to adopt a single overall deceleration rate from the diverge point to the end of deceleration. Finally, observations of data confirmed the importance of accounting for the effects of ramp controlling features on the diverging behavior and vehicle deceleration needs at freeway exit ramp terminals. The paper suggests that the design of deceleration SCLs should take into consideration the effect of ramp controlling features in the design of deceleration SCLs.
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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 | 0.000 |
| 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) | 0.001 | 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".