The optimization of lane assignment and signal timing at the tandem intersection with pre‐signal
Bibliographic record
Abstract
SUMMARY This paper presents an integrated model for optimizing lane assignment and signal timing at tandem intersection, which is introduced recently. The pre‐signal is utilized in the tandem intersection to reorganize the traffic flow; hence, the vehicles, regardless of whether left‐turns or through vehicles, can be discharged in all the lanes. However, the previous work does not consider the extra traffic disruption and the associated delay caused by the additional pre‐signal. In the paper, the extra delay aroused by the coordination is incorporated in a lane assignment and signal timing optimization model, and the problem is converted into a mixed‐integer non‐linear programming. A feasible directions method is hence introduced to solve the mixed‐integer non‐linear programming. The result of the optimization shows that the performance of the tandem intersection is improved and the average delay is minimized. The comparison between the tandem and the conventional configuration is presented, and the results verify that the former shows better performance than the latter. In addition, the optimal sequence corresponding to the turning proportion and the optimal lane assignment at the upstream approach of the pre‐signal are presented. Furthermore, if the number of lanes is equal in all arms, the paper proves that the average delay will be reduced if lane assignment is proportional to the turning proportion and the vehicles with low proportion are discharged in advance. Copyright © 2013 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".