Operational Evaluation of Vehicle Detection Systems at Rural Signalized Intersections
Bibliographic record
Abstract
Approaches considered to improve safety at rural high speed signalized intersections most likely will adversely affect the operational aspect of the intersection, and vice-versa. Vehicle Detection Systems and Advanced Warning Systems (AWS) have been used to end the green phase for the major road approaches in a safe manner and to warn drivers of an upcoming change of phase, respectively. If phase termination is by max-out, it will eliminate the expected safety benefit by ending the green phase without considering vehicles that may be traveling the dilemma zone. In order to address this concern, an intelligent detection control system (D-CS) was developed by Texas A&M University. The main feature of this system is to identify if trucks are located in the dilemma zone in order to extend the green beyond the maximum limit to allow them to safely cross the intersection. This research evaluates the D-CS and traditional vehicle detection systems in a Canadian environment. For this evaluation, operational and safety performance of both systems were determined and compared at high speed signalized intersections. This paper only presents results for the operational evaluation. Parameters considered for this evaluation include: control delay, percent of vehicles stopping on red, and percent of vehicles in the dilemma zone. For field data collection, video cameras were used to record actual data (traffic volume, signal timing, others) at two signalized intersections. Results indicated that the D-CS has a better operational performance than the traditional vehicle detection system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".