Operational Efficiency Evaluation of Intersections with Dynamic Lane Assignment Using Field Data
Bibliographic record
Abstract
The dynamic lane assignment at signalized intersections is a possible countermeasure to address the traffic demand variability problem. However, the operational efficiency is affected by the unfamiliarity of the drivers. This paper evaluates the operational efficiency of the intersections with dynamic lane assignment using field data collected at five intersections in China. A total of 63488 vehicles were captured, which were divided into four groups according to the lanes they drive on: Group 1, the variable approach lane; group 2, the lane adjacent to the variable lane with the same lane-use; group 3, the lane adjacent to the variable lane with different lane-use of the variable lane; and group 4, the lanes with the same lane-use as the variable lane at other approaches of the intersection. The statistical analysis was conducted to identify the difference of saturation flow rate among the four groups. A saturation flow rate adjustment model was established accordingly. Results indicate that the using of the dynamic lane assignment decreases the saturation flow rate of the variable lane and the adjacent lane with different lane-use 22.86% and 9.80%, respectively. For the variable lane, the reduction of the saturation flow rate comes from three aspects: the unequal distribution of traffic (8.9%), the mandatory lane-changing (10.7%), and the lane blockage (4.9%).
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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".