Spatiotemporal Decomposition and Analysis of Vehicular Arrival on Green in A Signalized Arterial using Trajectories
Bibliographic record
Abstract
Signal coordination is vital for smooth vehicle flow in corridors. Evaluating arterial progression typically relies on performance indices like travel time and delay, as well as tools such as the Purdue Coordination Diagram (PCD). However, these methods often overlook trajectory details and fail to capture continuous travel patterns. To quantify and visualize the arterial progression quality, this paper identifies spatially distinct vehicle groups using trajectory data and investigates the temporal travel patterns of each group. A velocity matrix of vehicles' travel along a corridor is extracted from the trajectories. Then a nonnegative matrix factorization (NMF) is utilized to reduce the dimensionality of the velocity matrix, which is decomposed into two matrices representing vehicle travel patterns and their weights. The resulting eigentravel pattern matrix indicates traffic flow smoothness and identifies co-occurrences of arrivals on green (AOG) or red (AOR) signals. Additionally, a novel metric called the Proportion of Arrival on Green (P-AOG) evaluates progression quality under different traffic control systems. The trajectory data are generated by the simulation software SUMO, and the simulation is based on six consecutive intersections of 23rd Avenue in Edmonton, Canada. A case study involving a before-and-after comparison of two signal control strategies is used for demonstration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".