Traffic Congestion in Dar es Salaam: Implications for Workers’ Productivity
Bibliographic record
Abstract
Inadequately planned transport systems result to traffic congestion, a challenge that has for long been a thorn in Dar es Salaam, the city most affected in Tanzania. Although traffic congestion has been a major concern in Dar es Salaam, marked reluctance has been noticed in taking measures towards a lasting solution thus, it is of diminutive surprise that limited studies and documentations on the same are in existence. Therefore, this study assesses traffic congestion in Dar es Salaam and particularly its implications for workers’ productivity. Travel time and productivity indexes were established from a sample of 96 workers who used public transport along Morogoro and Mandela Roads. Travel time index (TTI) is the ratio of the average travel time during peak period to the travel time during off-peak period. Findings reveal that TTI was 2.19. Workers spent about 2 times of the average commuting time to work and 3 times of the same commuting from work to their various residences. About 2.5 hours were lost on traffic jam per day and that people worked 1.4 times less than the required time due to traffic congestion. It was further established that in 10 working days, almost 3 days were lost to traffic congestion. Since there are ongoing efforts to improve the transport system through the Bus Rapid Transit (BRT) project, it remains to be seen as to what extent traffic congestion will be reduced. In either case, this study provides a benchmark for comparisons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".