Holistic approach to improve road traffic safety
Bibliographic record
Abstract
To take a further steps towards the long term vision of zero fatalities and to reach the targets set for Sweden (halving the number of traffic fatalities and reduce the number of seriously injured by a quarter from 2007 to 2020) and the EU (halving the number of road deaths between 2010 and 2020), it is necessary to focus the cause of accidents and through risk management as early as possible prevent its occurrence. By better understanding the causes of accidents, efficient solutions can be developed through a combination of safe vehicles, safe drivers and safe organizations. The project Holistic approach to improve road traffic safety, pre-study of a transport company has by a study of PostNord Logistics operations in Sweden investigated accidents in remote, regional and urban distribution and through cooperation between Volvo GTT and PostNord followed up and investigated the causes of accidents. This has been done by taking a holistic approach, where the investigation focus on vehicles, drivers and organization, all of which affect the risk of accidents. Through this work we have become more effective at understanding the causes of accidents, which is a prerequisite to developing effective solutions. For both truck and van accidents 43 % of the accidents occurred on public roads, mostly on roads within urbanized areas. 53 % of the accidents occurred at docks, in terminals, parking areas and courtyards. The accidents mostly occurred at low speeds when handling the vehicle in tight spaces where it is important to be aware of the vehicle's length, width and height. Of the accidents studied, 4% occurred on the road outside urbanized areas. The majority of these accidents happened on multilane roads and in higher speed. The lane departure accidents and rear-end collisions are recurring accidents types on roads outside built-up areas. 2% of the accidents resulted in personal injury. Two accidents were reversing accidents with pedestrians; two cases were rear-end collisions with another vehicle and one case a turning accident hitting a moped. To investigate the causes of the accidents that were identified in the project, PostNord Logistics was studies and analysed in the areas of safety priority, problem understanding and actions. Also specific problem areas for drivers in their work were identified. These include the driver's overall driving skills, procedure for vehicle handling at delivery and collection of goods, delivery reliability, conditions for work and motivation of drivers to drive safely. Furthermore, recommendations for actions that have the potential to reduce the risk of accidents at Post Nord Logistics were proposed. They can be summarized in activities within the organization, specifically for the drivers and for technical and vehicle solutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".