Characterizing and Estimating Operational Cost Elements and Performance Metrics for Public Transport Modes in Sub-Saharan African Cities
Bibliographic record
Abstract
Operation cost of public transport has been universally recognized as a vital consideration in its implementation, operation and management worldwide. The cost structure is even more complex in Sub-Saharan Africa (SSA), where the public transport system is dominated by paratransit, which is largely characterized by the private sector that owns, operates, maintains, and manages their vehicles separately. There is limited knowledge of paratransit cost structure, the extent to which they vary by mode, and their use in fare-setting and the design of regulatory policies. Accordingly, this study characterized and quantified several operational cost elements in the form of Key Performance Indicators (KPIs) for paratransit modes in two SSA cities: Accra-Ghana and Dar es Salaam-Tanzania. Vehicle-make in Accra-Ghana is mainly Mercedes Benz and Toyota, whereas those in Dar es Salaam-Tanzania are Toyota, Nissan, and Bajaji tricycles. Diesel is the most popular vehicle propulsion followed by petrol in both cities. Our findings show higher vehicle utilization and better fuel consumption in Dar es Salaam-Tanzania compared to Accra-Ghana. Findings further indicate higher economic and environmental efficiency in minibuses compared to taxis and three-wheelers, with Dar es Salaam-Tanzania achieving better outcomes than Accra-Ghana in revenue generation, fuel consumption, and safety conditions. Daily revenue generation is higher, 75.1USD in Dar es Salaam-Tanzania compared to 46.3USD in Accra-Ghana. The overall expenditure is higher, 0.41USD/km in Accra-Ghana than 0.28USD/km in Dar es Salaam-Tanzania. Safety-related KPIs including kilometers per accident, kilometers per mechanical breakdown, and kilometers per tyre carcase show favourable conditions in Dar es Salaam-Tanzania compared to Accra-Ghana. These insights provide essential data to inform fare-setting, regulatory policies, and operational management of paratransit services in SSA. Overall, this study has established the cost structure for paratransit modes in the study cities, making available vital data and knowledge to underpin practical engagement, operational management, and governance of paratransit services in SSA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".