Development of a Knowledge Base for Multiyear Infrastructure Planning for Connected and Automated Vehicles
Bibliographic record
Abstract
Connected and automated vehicles (CAVs) require proper infrastructure for safer and more reliable operations. Many state and local planning agencies have developed multiyear capital programs to provide such infrastructure in a timely manner within their limited budgets. Meanwhile, the traffic environment will evolve over time as CAV technologies become available (i.e., toward the mixed environment of CAVs and human-driven vehicles), which requires infrastructure plans specific to different planning terms (i.e., short-, medium-, and long-term) to accommodate changing infrastructure needs. To develop an effective multiyear infrastructure plan, planning agencies need to understand changing infrastructure needs with time, identify alternative infrastructure options for different planning terms, and select the most appropriate ones based on their long-term vision. This study performed a systematic literature review to develop a knowledge base for multiyear infrastructure planning for CAVs. To be more specific, the literature review aims to develop the following knowledge areas: (1) identification of existing and future infrastructure options for the operation of CAVs, (2) understanding the role of infrastructure to support different functions of CAVs to realize safety, mobility, and environmental benefits, and (3) integration of the aforementioned findings into planning agencies’ multiyear infrastructure plans for CAVs. Based on the review, this study categorizes different CAV infrastructure into existing infrastructure and future infrastructure options while considering five system functions of CAVs (i.e., cooperative merging, platooning, intersection movement, dynamic routing, and cooperation and connected functions) to illustrate the role of these infrastructure options under different traffic scenarios. The implementation of the developed knowledge base is demonstrated through a case study of two selected state agencies’ long-term infrastructure planning for CAVs.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.039 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".