Methodology for analysing vehicle trajectory and relation to geometric design of highways
Bibliographic record
Abstract
With the increased emphasis on traffic safety in recent years, vehicle performance, in terms of speed, acceleration, braking, and cornering capabilities, has been improved through extensive research.As a result, driver behaviour has evolved greatly, and drivers expect better roads and higher design speeds. However, highway geometric design criteria in most design guides have not kept pace with these vehicle improvements. It is important to study the effect of geometric features of highways especially horizontal curves on driver behaviour. This will allow better design of highways and hence would improve traffic safety. The concept of highway design, with driver behaviour as one of its main parameters, has been gaining wider acceptance among highway professionals as an effective proactive tool to improve traffic safety. However, despite the importance of driver behaviour in vehicle and road design and despite the work expended on this issue, a comprehensive understanding of driver behaviour is still lacking.The research in hand attemptsto highlight points of deficiency in geometric design of highways that, through revision, could result in increased safety on roads. In doing so, data collected from a driving experiment are used to analyze driving behaviour, and an attempt will be made to incorporate the effect of driving behaviour to the geometric design of highways. This paper will mainly focus on driving behaviour data in terms of vehicle trajectory, steering angle , and rate of change of steering angle.The results are shown for different curves on two-lane two-way highways and freeways in Ottawa.The analysis shows that the results are promising and further analysis will result in improvements in highway design. (A)
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".