Accommodation of Freeway Merging in Environment of Mixed Vehicle Technologies
Bibliographic record
Abstract
The connected autonomous vehicle (CAV) is the most significant breakthrough in the automobile industry since the wide adoption of automobiles as a mode of transportation.These vehicles can respond faster, drive more precisely within their lane, and keep gaps shorter than driver-operated vehicles (DVs).Therefore, they can improve traffic operations and reduce collisions resulting from human errors.However, shorter gaps on the freeway right lane (FRL) can create difficulty for on-ramp vehicles to merge onto the freeway.In addition, there is a transition period from all DVs to all CAVs.As a result, there would be a mixed traffic environment including DVs and CAVs, which might necessitate different merging management strategies.This study proposes eight possible solutions to address the merging problems by providing acceptable gaps or resolving the conflicts between merging and mainline vehicles in a mixed traffic environment.These strategies are then evaluated based on different measures to determine which merging solution is most effective for each traffic condition.Average travel times and the capacity drop are used as traffic performance measures, while safety measures include the percentage of vehicles with low merging speed (VLMS) and probability of non-compliance (PNC) of merging manoeuvre.The behaviour of CAVs and performance merging strategies are modelled using Vissim v2020, internal programing, and a MATLAB program.The simulation results indicate that when the CAV penetration rate is between 0% and 100%, most proposed strategies outperform the base condition of do-nothing.The traffic performance changes depending on the CAV penetration rate and the type of traffic management strategy.iii The results of the safety measure for most strategies based on the VLMS index indicate that at low traffic volumes, increasing CAV penetration rate reduces VLMS, thereby improving safety.At high traffic volume, some strategies such as dissolving platoons and ramp metering show better performance.For the PNC measure, most strategies have lower PNC and better expected safety performance than the base strategy.Furthermore, increasing the CAV penetration rate reduces PNC.Finally, a regression analysis between PNC and the existing collision database demonstrated a correlation between PNC and collision frequency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".