Modeling Driver Behavior and Safety on Freeway Merging Areas
Bibliographic record
Abstract
Access to freeways is provided through interchanges to maintain the design concept of uninterrupted traffic flow. Therefore, provision of an appropriate entrance ramp and acceleration lane geometry that allows the entering vehicle to accelerate to a speed closer to the through lane speed is important for comfortable and safe merging maneuvers. In this paper, speed and traffic data were collected from 23 merging sites on Highway 417 located within the City of Ottawa, Canada, to study the traffic behavior at freeway merge areas. Analysis of traffic behavior showed that merging speed depends on both ramp and speed-change lane (SCL) geometrics. Lower merging speed was shown to be associated with higher collisions on the acceleration lanes. Right lane traffic volume and merging speed of entering vehicles were shown to significantly affect right lane speed along the acceleration lane. In addition, several statistically significant models were developed for the prediction of 85th percentile passenger car right lane speed, merging speed, merging distance, and acceleration on the SCL. A safety performance model was also developed to relate the total number of collisions on the acceleration SCL to the features of the merge area including the merging speed.
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.
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 | 0.001 | 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 | 0.000 |
| 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".