A Group-Based Signal Timing Optimization Model Considering Safety for Signalized Intersections with Mixed Traffic Flows
Bibliographic record
Abstract
The conventional stage-based signal control approach with uniform phase structure has been dominantly applied at signalized intersections in China. However, this approach cannot efficiently handle mixed traffic flows with unbalanced volumes. Moreover, this signal control approach has resulted in many safety issues, such as traffic conflicts (a) between the right-turning motorized vehicles and the straight-through bicycles and (b) at the change of phases due to bicycles’ clearance failure. Hence, the objective of this paper is to propose a group-based signal optimization model that considers both safety and delay for the intersections with mixed traffic flows. In the proposed model, safety was evaluated based on the traffic conflicts during the inter-green period and was incorporated into the signal timing procedure. A probabilistic approach was developed to estimate the probability of occurrence of conflicts, with a novel safety indicator combining postencroachment time and kinetic energy for measuring conflict severity. The average delay per person, according to the Highway Capacity Manual 2010 method, was adopted in this paper. Then, the multiobjective optimization issue was formulated as a nonlinear program and solved by a Nondominated Sorting Genetic Algorithm. A numerical study was performed to demonstrate the applicability and performance of the proposed model. Results indicated that the proposed model can provide an effective tool for researchers and practitioners to simultaneously optimize traffic safety and efficiency in signal planning. It may also overcome the disadvantages of most of the conventional models, which are incapable of quantifying safety in the optimization process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".