Development of a Knowledge Base for Multiyear Infrastructure Planning for Connected and Automated Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it