Probabilistic Analysis of Intersection Sight Distance at Uncontrolled and Yield-Controlled Intersections in Mixed Vehicle Environments
Bibliographic record
Abstract
The imminent introduction of autonomous vehicles (AVs) and mixing with driver-operated vehicles (DVs) in the traffic environment warrant research to determine the impacts on current intersection sight distance (ISD) designs.This research used a probabilistic method to analyze the ISD at uncontrolled and yield-controlled intersections associated with a mixed traffic of DVs and AVs.Several existing and developed DV and AV models were used as the basis of overall system demand and supply models.ISD non-compliance occurred when the demand was greater than the supply.The probability of unresolved conflict (PUC) measure was developed to estimate the level of non-compliance of object locations.This surrogate safety measure was used since the low numbers of uncontrolled and yield-controlled intersections combined with low traffic volumes produce low collisions, making it challenging to have an explicit safety relationship to expected collision frequency.PUC combined the results of a Poisson-based conflict estimation model and the probability of non-compliance (PNC) of the vehicle interactions obtained by Monte Carlo Simulation (MCS) and the lower traffic volume of the intersecting roads to represent conflicts that need evasive action by at least one vehicle beyond the identified driving behaviour values to prevent a collision.PUC values of object locations were developed for the DV-only and mixed traffic environments.These values were presented for different speed combinations, AV penetration rates, and traffic volume combinations.PUC values for the DV-only environment were referenced to a clear sight distance triangle (SDT) based on AASHTO designs.In any analysis scenario, the highest PUC value outside this triangular area was considered a target PUC value that can be used to establish design guidelines under mixed traffic conditions, and a mixed traffic PUC value should be lower ii than the associated target PUC value.However, the analysis of specific scenarios for uncontrolled and yield-controlled intersections showed that the operation of a mixed vehicle environment was inconsistent with the current AASHTO guidelines.The solution to this issue was to reduce the PUC values through managing AV speeds.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".