Effects of Urban Expansion on Transport Systems in Ondo Town, Ondo State, Nigeria
Bibliographic record
Abstract
Ondo town has experienced a significant expansion in land use, which has influence on transport system. Thus, the study examined how urban expansion has affected transportation such as traffic congestion, travel times, transportation costs, how transport sector has benefited from the expansion, and how urban planning and policy interventions can mitigate the negative effects of urban expansion on transport system in Ondo town. Random sampling technique was employed to select 200 respondents with the minimum age of 18 years old for questionnaire administration within the stratified 10 quarters of the study area. Twenty copies of questionnaire were administered in each quarter, making 200. Four points Likert scale was used to acquire primary data on influence of urban expansion on transport system. While the study employed descriptive method for data analysis. The study revealed that most respondents (34%) commute to their workplaces by commercial motorcycles. The study also revealed that more than half of respondents (53%) strongly agreed that urban expansion in the study area has led to increase in traffic congestion. the study showed that majority of respondents (47%) agreed that expansion of land use in the city increased road crashes leading to injuries, defame and casualties. Despite these challenges, findings showed that 45% of respondents strongly agreed that urban expansion has increased accessibility to employment opportunities in transport sector. The study recommends that as cities grow and expand in land use, transport infrastructure should also be enhanced simultaneously such as installation of traffic management systems to manage traffic flow. Received: 5 April 2025 / Accepted: 20 April 2025 / Published: 10 May 2025
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".