Effectiveness Evaluation of Signal Coordination Based on Spatially Sparse Trajectory Data
Bibliographic record
Abstract
Signal coordination is an effective measure to improve the traffic efficiency of urban road networks, and network partition is an important part of it. Existing studies have proposed indicators based on the characteristics of arterial geometry and traffic flow to determine adjacent intersections that are suitable for signal coordination. However, it is difficult to explicitly identify the benefits and thus the necessity of signal coordination with these indirect indicators. This study defines Intersection Coordination Index (ICI) to evaluate the potential effectiveness of arterial signal coordination. ICI explicitly considers signal timing plans at each intersection and implicitly considers the impacts of the characteristics of arterial geometry and traffic flow. An offset optimization model is formulated to calculate ICI based on sampled trajectories of connected vehicles (CVs). It is a MILP model and can be efficiently solved by existing solvers. To cope with the low penetration rate of CVs, sampled trajectories are aggregated during the same period across multiple cycles. Numerical studies show: the proposed model is adapted to the low penetration rate trajectory environment; the dispersion of arriving vehicles at the downstream intersection reduces the benefits of signal coordination; and ICI outperforms the benchmark indicators in terms of the average cost of delay.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".