Safety Assessment of the Integration of Road Weather Information Systems and Variable Message Signs in British Columbia
Bibliographic record
Abstract
Adverse weather conditions create an environment in which it is difficult for drivers to navigate safely. Reducing weather-related collisions is a target for road safety professionals in British Columbia (BC), Canada. This study reports the safety benefits of installing road weather information systems (RWISs) coupled with variable message signs (VMSs) on provincial rural highways in BC. The RWIS/VMS system comprises road and weather sensors, as well as two VMSs. The road and weather sensors collect data on pavement surfaces and weather conditions. Information on adverse road/weather conditions are conveyed to road users via the VMSs. The system had been installed at six different locations on rural undivided highways in BC between 2011 and 2014. The analysis made use of police-attended serious crashes (i.e., fatal + injury) that took place during winter seasons. Depending on the implementation date, three or four winter seasons were available as a before-implementation period, while three to six winter seasons were available as an after-implementation period. An Empirical Bayes (EB) approach was employed to ensure that the evaluation results were reliable and to account for the regression-to-the-mean artifact. Safety performance functions (SPFs) were developed using data collected at similar sites. The EB evaluation results showed an overall statistically significant reduction of 32.7% in all winter serious collisions (WSC). An economic evaluation showed that the systems led to a benefit-cost ratio of 4.8 and an overall net present value of more than Can$12 million. The results of this study may motivate transportation agencies and stakeholders to pursue similar systems for mitigating weather-related safety problems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".