Traffic Mitigation And Congestion In Ibadan, Oyo State Nigeria: Causes And Solutions
Bibliographic record
Abstract
This dissertation focuses on how technology has transformed road traffic congestion between road users by examining the relationship between government, businesses, and road users through the application of traffic management in many cities in four regions of the world. It analyzes the connections between political decisions of traffic management, how users are made knowledgeable and the new modes of transportation as they relate to the roles of public and private users. The dissertation examines current literatures of twenty two (23) different countries: North America (United States of America and Canada), Europe (Poland, United Kingdom, Italy, Netherlands and Sweden), Asia (Russia, China, Singapore, Hongkong, United Arab Emirates, Israel, South Korea, Japan, India), and West Africa (Ghana, Senegal, Sierra Leone, Mali, Cote D’Ivoire, Nigeria) as they relate to trends and causes of congestion and mitigation applied in each country. Finally, this dissertation takes an in depth look at how road network can be improved through mitigation to reduce congestion in Nigeria; most specifically in Ibadan focusing on the intersection of Sango Eleyele Road and Sango Ojoo Road representing most of the major intersections in Ibadan, Oyo State Nigeria.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".