Modeling the Impact of Urban Land Use Characteristics on Road Network Accessibility in a Case Study of Addis Ababa City
Bibliographic record
Abstract
Rapid urbanization in emerging nations such as Ethiopia encourages participants in the area to consider the sustainability and balanced growth of metropolitan centers from a variety of angles and to offer suitable solutions. This study aims to comprehend how urban land use influences the efficiency and connection of road networks, which in turn affects how accessible different parts of the city are, to determine the influencing factors and critical indicators that enable a systematic categorization of subcity accessibility from the perspectives of urban land use. Geographic information system (GIS) techniques are used to gather and analyze precise geographical data on road networks, land use patterns, and other pertinent properties. In addition, a regression model is created to measure the effect of land use features on the accessibility of the road network. The primary questionnaire data are used to estimate the current situation of road accessibility and are used in the study validation process. This research has incorporated qualitative and quantitative approaches to sustainability. The findings imply that residential areas are 90% significant in making transport along the city, and 98% of the accessibility of the road network is dependent on land use features, including road density and subcity areas. Among the subcities, Bole, Kirkos, and Arada have 87% road network accessibility and better access to public transportation. Overall, these techniques emphasize the value of spatial analytic tools in planning procedures to better comprehend and control the intricate relationships between transportation and land use dynamics and to assist in the sustainable design and growth of cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".