Adaptive Traffic Signal Control Pilot Project for the City of Surrey
Bibliographic record
Abstract
The City of Surrey has been the fastest-growing municipality in British Columbia over the past decade, and is on pace to surpass Vancouver as BC's largest city sometime over the next twenty years. With this continued rapid growth, there is a growing need for a better, more cost-effective method to more efficiently manage the traffic demand. The corridor selected for the Adaptive Traffic Signal Control (ATSC) Pilot Project was 72nd Avenue, between 120th Street and King George Boulevard. The seven closely spaced signalized intersections along 72nd Avenue are controlled by the City's BiTrans Type 170 traffic signal controllers, and monitored by the City's McCain QuicNet traffic signal management system. The scope of the ATSC Pilot Project was to demonstrate the integration of traffic adaptive control with the City's existing traffic signal control infrastructure, and to evaluate the benefits of adaptive control. The ATSC system's open system architecture is flexible to work with the City's existing Type 170 controllers, vehicle detector loops, and communications network. This paper describes the real world application of an ITS system designed to improve traffic operations, including lessons learned. The pilot project demonstrated the seamless integration of the ATSC system with the City's existing traffic signal control infrastructure. The field surveys demonstrated that adaptive traffic signal control performed equal to the best optimized TBC signal timing plans during the peak traffic periods, and was able to effectively adjust to unexpected traffic patterns during off-peak periods. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".