Improved Time-to-Collision Definition for Simulating Traffic Conflicts on Truck-Only Infrastructure
Bibliographic record
Abstract
Transportation agencies have traditionally relied on historical crash records as the primary measure to evaluate the safety of roadways. The infrequent and sporadic occurrence of accidents and the long period required to collect accident data have led to the use of surrogate safety measures. The use of microsimulation modeling for conflict analysis has been popularized for evaluating experimental changes to existing road networks. Previous freeway studies have used a simplified time-to-collision definition, which produces unrealistic conflict situations. The definition included situations with no collision path, such as when two vehicles were traveling at the same speed or when the leading vehicle was speeding away from the following vehicle. A revised conflict definition is developed to address these issues and is then contrasted with the simplified definition used in earlier studies. An investigation of acceleration rates demonstrates that the revised approach retains the meaningful conflicts produced by the previous definition but eliminates the situations that are unlikely to be conflicts. This revised conflict definition is used to investigate the evaluation of a truck-only highway in the greater Toronto, Ontario, Canada, area to observe the effects on traffic conflicts. In general, it was found that although providing a separate highway for trucks did reduce truck-related conflicts, car lane-change conflicts increased because of the cars' increased maneuverability and presence on the truck-free highway.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".