A Cooperative Intelligent Transport Systems (C‐ITS)‐based lane‐changing advisory for weaving sections
Bibliographic record
Abstract
Summary Weaving sections, a common design of motorways, require extensive lane‐change manoeuvres. Numerous studies have found that drivers tend to make their lane changes as soon as they enter the weaving section, as the traffic volume increases. Congestion builds up as a result of this high lane‐changing concentration. Importantly, such congestion also limits the use of existing infrastructure, the weaving section downstream. This behaviour thus affects both safety and operational aspects. The potential tool for managing motorways effectively and efficiently is cooperative intelligent transport systems (C‐ITS). This research investigates a lane‐change distribution advisory application based on C‐ITS for weaving vehicles in weaving sections. The objective of this research is to alleviate the lane‐changing concentration problem by coordinating weaving vehicles to ensure that such lane‐changing activities are evenly distributed over the existing weaving length. This is achieved by sending individual messages to drivers based on their location to advise them when to start their lane change. The research applied a microscopic simulation in aimsun to evaluate the proposed strategy's effectiveness in a one‐sided ramp weave. The proposed strategy was evaluated using different weaving advisory proportions, traffic demands and penetration rates. The evaluation revealed that the proposed lane‐changing advisory has the potential to significantly improve delay. Copyright © 2016 John Wiley & Sons, Ltd.
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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".