Road Engineering Determinants of Moped and Motorcycle Crashes at Non-Intersection Road Segments in a Developing Country – Mauritius
Bibliographic record
Abstract
The road safety situation is extremely alarming in developing countries where 93% of the worldwide 1.35 millions road crash fatalities occur annually. As in most Low and Medium Income Countries (LMICs), in Mauritius also, powered-two-wheeler (PTW) riders are the most vulnerable road-user category with respect to road crash fatalities and serious injuries. Despite the application of enforcement and education counter-measures for decades the afore-mentioned situation does not ameliorate. With reference to the Safe System approach there is a need for a more holistic approach to road safety whereby the road and the road environment factors of PTW crash risks need to be considered in parallel with road-user and vehicle factors. This research aimed at amplifying awareness about the road engineering factors of PTW crash severity in a developing country context. With this focus the objective of this study was to identify the road engineering determinants of mopeds and motorcycles crash severity at non-intersection road segments in Mauritius. Police-reported data for 3,103 PTW injury crashes over the 3year period 2014 -2016 was used for the analysis. The Logistic Regression method was used with a dichotomous dependent variable; 'crash severity' and 10 explanatory variables. Specific categories of the 'Road Hierarchy', 'Pillion Rider' and 'Crash Partner' factors were found to be relevant to occurrence of fatal and serious injury crashes for motorcycle at road mid-block. For mopeds, horizontal curves from the 'Road Alignment' factor was found to be the most hazardous road configuration. Three common factors for both PTW types were identified; 'Light Condition', 'Crash Time' and 'Crash Type' but with partly distinct categories. The variables 'Crash Day', 'Weather Condition' and 'PTW Manoeuvre' were not related to crash severity of neither PTW type. The findings of this study can be used by road authorities to improve road safety along non-intersection road segments in developing countries, particularly for PTW riders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".