Implications of a Narrow Automated Vehicle-Exclusive Lane on Interstate 15 Express Lanes
Bibliographic record
Abstract
The main objective of this study is to evaluate the safety and operational impacts of an innovative infrastructure solution for safe and efficient integration of Automated Vehicle (AV) as an emerging technology into an existing transportation system. Filling the gap in the limited research on the effect of AV technology on infrastructure standards, this study investigates implications of adding a narrow reversible AV-exclusive lane to the existing configuration of I-15 expressway in San Diego, resulting in a 9 ft AV reversible lane and, in both directions, two 12-feet lanes for HOV and FasTrak vehicles. Given the difference between the operation of AVs and human-driven vehicles and reliance of AVs on sensors as opposed to human capabilities, the question is should we provide narrower AV-exclusive roadways assuming AVs are more precise in lateral and longitudinal lane keeping behaviour? To accomplish the goal of the project, a historical crash data analysis and a traffic simulation analysis were conducted. Crash data analysis revealed that unsafe speed, improper turning, and unsafe lane change are the most recurring primary collision factors on I-15 ELs. AVs’ automated longitudinal and lateral control systems could potentially reduce these types of collisions on an AV-exclusive lane with proper infrastructure features for AV sensor operation (e.g., distinct lane marking). Microsimulation findings indicated an AV-exclusive lane may increase traffic flow and density by up to 14% and 24%, respectively. It also showed that average speed is reduced. However, this could lead to the speed differential increase between the exclusive lane and adjacent lane requiring careful consideration if additional treatments or barriers are needed. The results of this study contribute to infrastructure adaptation to AV technology and future AV-exclusive lanes implementations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".