Service Level Evaluation of Floridas Highways Considering the Impact of Autonomous Vehicles
Bibliographic record
Abstract
Service Level Evaluation of Florida's Highways Considering the Impact of Autonomous Vehicles Amirsaman Mahdavian, Alireza Shojaei and Amr Oloufa Pages 1163-1170 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Automated vehicles (AV) are undergoing development at a remarkable pace and have the potential to revolutionize the existing transportation system. The ASCE [1] evaluated the United States' infrastructure as a D+ grade. Moreover, they predicted radical infrastructure investment gaps in the surface transportation sector in the upcoming years. Some new urbanized regions might require new highways. Meanwhile, many other highways are reaching the end of their service life and will need significant repairs or even replacement. However, this seems to be unrealistic to happen until having a high market penetration of Fully Connected and Autonomous vehicles on the road to benefit from the capacity expansion benefits. Regarding the funding related issues of highway construction in the U.S. and the emergence of AVs, having a better understanding of the future's traffic status is a must. This study investigates the impact of autonomous vehicles on Florida's district five of I-95 highway traffic including three counties: Flagler, Volusia, and Brevard. This research is the first study to develop a fusion model considering the impact of both traffic flow and capacity adjustments based on the literature review to forecast the traffic from 2020 to 2040 by considering the increasing AV market penetration. The proposed approach provides a more realistic plan for government agencies and private investors, and as a result, significant savings financially and resource-wise can be achieved. The findings of the study confirm that autonomous vehicles will increase traffic flow and capacity, and the increase in flow is higher than the increase in capacity. Keywords: Autonomous Vehicles; Traffic Flow and Capacity; Long-term Planning; Highway DOI: https://doi.org/10.22260/ISARC2019/0155 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".