Study on Calibration and Validation of Fundamental Diagram for Urban Arterials
Bibliographic record
Abstract
Fundamental diagram (FD) describes the relationship between the macroscopic traffic parameters - traffic speed, flow and density. This study aimed at investigating the existence and properties of fundamental diagrams on urban arterials. The research first investigated the efficacy of estimating speed-flow relationship on 97 Street, one of the major arterials in the City of Edmonton, using the Webster’s delay formula. Then the existence of arterial fundamental diagram was investigated by analyzing the field data collected from the groundhog sensors installed on the study corridor. Finally, this study also explored several traffic flow characteristics by analyzing the speed-flow-density diagrams obtained from the micro-simulation model of 97 Street. The results from the theoretical analysis showed that the Webster’s delay formula could be successfully used to estimate the speed-flow relationship on any under-saturated signalized segment of urban arterials. The existence of an arterial fundamental diagram linking flow and density was also observed from the field data. The outcome of the micro-simulation model supported these results, and thus confirmed the existence of FD on any arterial segment. However, both the maximum flow and the maximum density from the simulations results were observed to be slightly larger than those obtained from the groundhog data. When the data from individual segment were aggregated to investigate the speed-flow-density relationships for the whole study corridor, somewhat chaotic scatter-plots were found. The presence of traffic signals might be the reason for this scatter.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".