A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR TRAFFIC SIGNAL DESIGN
Bibliographic record
Abstract
Traffic signal design requires experienced traffic engineers to decide on the phasing plans and then timing plans can be optimized. However, the available guidance provides general recommendations for phasing planning; while, commercial software optimizes the timing plans and corresponding operation performance (e.g., best performance index). Furthermore, the performance index excludes safety factors. This research aims to develop a framework that can deliver a phasing plan by enumerating all possible solutions and then selecting the phase plan that achieves the objectives. These objectives are to minimize the severity, average delay, and queue length. A program (i.e., software) has been developed to perform these stages and deliver a sorted list of phase plans based on the assessment criteria considering different traffic patterns, different intersection lane configurations, and operation types for left and right turns. A validation exercise was performed to assess the effectiveness and practicality of the developed software. It includes designing a phase plan for fifteen American, Canadian, and Chinese intersections and comparing them against their actual phasing design in terms of safety, average delay, and queue length. The results show that on average, the safety level is improved by 19%; however, the delay and queue length increased by 33% and 13%, respectively. The results encourage extending the proposed framework to consider the phasing sequence and coordination in future work. Finally, it is found that the framework provides practical phasing plans in terms of safety and other operational aspects. The results encourage extending the proposed framework to consider the phasing sequence and coordination in future work. Keywords: Traffic Signal Optimization, Phasing Plan, Highway Safety Manual, Queue Length DOI： https://doi.org/10.35741/issn.0258-2724.58.1.37
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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".