The Impact of Urban Growth Pattern on Local Road Network: A System Dynamics Study
Bibliographic record
Abstract
The complex nature of urban growth in cities whose population is exponentially increasing requires a comprehensive understanding to create a precise and descriptive modelling. In order to identify the main factors that influence the behavior of such complex growth and consequently recognize the most applicable future projection to the growth in each urban category, a system dynamics model was developed in which all pertinent variables are incorporated. This model was proven to be capable of simulating the urban growth in Baquba city for some six decades from 1957 to 2017. The simulation results showed very high goodness of fit with the historical records with an R2 ranging between 0.987 and 0.997 proving the validity and applicability of the model. The interaction between various urban categories showed that the road network area was negatively influenced mainly by the rapid growth of residential and public areas. The future projections of this model to the target year of 2035 showed that the residential, public, commercial and industrial categories are increasing by; 55%, 84%, 40%, and 19% respectively. The road area has also increased by 19% in the same projection gaining more expansion than what it got in the last three decades prior to 2017.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".