Mapping the Jitney Network with Smartphones in Accra, Ghana: The AccraMobile Experiment
Bibliographic record
Abstract
A data collection exercise is presented that was conducted by the Department of Transport of the Metropolitan Assembly of Accra, Ghana, to further its knowledge of transportation services placed under its jurisdiction. In order to map the city’s transportation network, a partnership was developed between local authorities and a Canadian university with the support of the French bilateral development agency. An innovative methodology based on the use of smartphones and digital technologies allowed the project team to collect and map 315 jitney routes in less than 2 months. Collectors equipped with GPS-enabled smartphones surveyed Accra’s formal jitney network in its entirety and transmitted data daily to a team overseas in charge of mapping and analysis. The first map of the city’s transportation network is presented here and preliminary conclusions are drawn from it. By mapping passengers’ boarding and alighting, this study also offers unique insights into the spatial distribution of the demand for transportation in Accra. This research opens both methodological and operational perspectives. It contributes to a growing body of literature on jitneys and transportation planning in developing countries. It also demonstrates that transportation data can be collected with limited time and resources through the use of mobile technologies. From a practical point of view, these data will assist the authorities in regulating, planning, and developing Accra’s transportation network.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".