Simulation of Signalized Intersection with Non-Lane-Based Heterogeneous Traffic Conditions Using Cellular Automata
Bibliographic record
Abstract
Intersections affect the maneuvering and driving behavior of vehicles. The present study attempts to simulate an isolated signalized intersection with the dimensions obtained through the influence zone of intersections. This model includes several unexplored traffic characteristics observed at the intersection, such as non-lane-based heterogeneity and seepage behavior. The model was calibrated and validated with the field data collected in New Delhi, India. Several measures of performance, such as GEH statistics, Theil’s coefficient, root mean square error, and so forth, were used to validate and benchmark the simulation model. After calibration and validation, the model was used to find delays. The delays obtained from the model, several manuals, and the field were compared and found to be close to the field delays. Further, delays obtained from Indonesian and Canadian manuals were comparatively closer to the delays obtained from the field, whereas delays obtained from the Indian Highway Capacity Manual (2017) and U.S. Highway Capacity Manual (2010) are overestimated. The model presented can be used to benchmark the performance of signalized intersections under a variety of traffic and environmental conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".