Evaluating the Need for Traffic Signal Retiming Using Connected Vehicle Data
Bibliographic record
Abstract
The emergence of automated traffic signal performance measures (ATSPMs), aided by recent technological advancements, allows for continuous traffic performance monitoring, and supports traffic agencies in taking proactive measures. High-resolution trajectory data from connected vehicles (CVs) has surfaced as a cost-effective method for assessing ATSPMs. Although various metrics have been developed to measure traffic signal performance, none have been specifically designed to estimate the benefits of signal retiming. This study devises a novel methodology to estimate the potential reduction in overall intersection delay resulting from signal retiming using only CV data. This methodology results in a new metric, the traffic signal suboptimality index, that uniquely estimates the potential avoidable delay rather than simply the observed signal delay. This new metric could enable traffic agencies to predict the benefits of potential signal retiming without the need for conducting costly traffic surveys and help these agencies prioritize locations and times of day for signal retiming. This study employs the VISSIM microsimulation software to implement and evaluate the methodology under various traffic scenarios and CV market penetration rates. In our experiments, the proposed methodology successfully detected signal retiming needs in situations involving an imbalanced degree of saturation, traffic demand fluctuations on competing movements, and changes in traffic direction, even with CV penetration rates as low as 10%. Furthermore, the sensitivity analysis reveals that the temporal aggregation period can be increased to further compensate for low CV penetration rates.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".