Cellular automata model for heterogeneous traffic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cellular Automata (CA) modelling is extended to study the heterogeneous traffic observed in developing countries. In heterogeneous traffic, the physical and mechanical characteristics of different vehicles vary widely which in turn leads to complex traffic behaviour resulting in no‐lane discipline. This nature of the heterogeneous traffic is modelled with the help of an improved discrete CA model. A detailed description of the methodology used in developing the basic structure of the CA model is presented and the modified methodology is used to generate different traffic scenarios. From the results, it is observed that with the help of simple updating rules along with typical heterogeneous traffic characteristics of the region, this model is able to reproduce real traffic behaviour. An added advantage is that the modified structure of the CA model can also be used to extract some basic traffic characteristics which are useful in understanding the heterogeneous traffic behaviour. The simulation model is finally validated using the flow and occupancy relationship obtained from the field.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it