Exploring barriers and perceptions to walking and cycling in Nairobi metropolitan area
Bibliographic record
Abstract
Introduction Walking and cycling as a form of active travel offer an opportunity for individuals to engage in physical exercises while performing a functional journey. Notwithstanding, the large proportion of the population relying on non-motorized transport (NMT), namely walking and cycling, has not been prioritized. At a time when lifestyle health challenges such as obesity and other non-communicable diseases are on the rise, walking and cycling would provide a window of opportunity and potentially provide exercise and thus improve the general health and wellbeing of the population. More than 75% of total daily trips made by Africa's low-income population are made by walking, compared with 45% by the more affluent people. Walking and cycling, considered low-carbon emission modes of transport, not only enhance urban quality but also boost social cohesion. Despite these potential gains, poor NMT infrastructure systems, low integration with the other modes of transport, and non-committal by law enforcement to protect pedestrians and cyclists still define the NMT ecosystem. Methods This study used descriptive methods to explore the barriers to and citizen perceptions of walking and cycling in Kenya's capital, the Nairobi Metropolitan area. Results and discussion Poor or absence of proper NMT infrastructure systems, safety concerns due to poor planning, lack of targeted policies as well as low or no capacity to ride a bicycle are among the predominant factors that undermine the use of NMT in Nairobi. However, a majority of citizens find no positive link between walking and/or cycling and poverty, a significant deviation from some prior studies and report. Recommendations Other than scaling up walking and cycling facilities, this study strongly recommends the use of participatory city frameworks to support NMT research, transport policy, and the needs of those already using walking and cycling as active modes of transport.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".