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Record W2159172367 · doi:10.1186/1476-069x-8-47

The impact of transportation infrastructure on bicycling injuries and crashes: a review of the literature

2009· review· en· W2159172367 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Health · 2009
Typereview
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCUniversity of British ColumbiaHeart and Stroke Foundation of Canada
KeywordsTransport engineeringSAFERPoison controlOccupational safety and healthInjury preventionEnvironmental healthHuman factors and ergonomicsCrashPopulationSuicide preventionEngineeringMedicineComputer scienceComputer security

Abstract

fetched live from OpenAlex

BACKGROUND: Bicycling has the potential to improve fitness, diminish obesity, and reduce noise, air pollution, and greenhouse gases associated with travel. However, bicyclists incur a higher risk of injuries requiring hospitalization than motor vehicle occupants. Therefore, understanding ways of making bicycling safer and increasing rates of bicycling are important to improving population health. There is a growing body of research examining transportation infrastructure and the risk of injury to bicyclists. METHODS: We reviewed studies of the impact of transportation infrastructure on bicyclist safety. The results were tabulated within two categories of infrastructure, namely that at intersections (e.g. roundabouts, traffic lights) or between intersections on "straightaways" (e.g. bike lanes or paths). To assess safety, studies examining the following outcomes were included: injuries; injury severity; and crashes (collisions and/or falls). RESULTS: The literature to date on transportation infrastructure and cyclist safety is limited by the incomplete range of facilities studied and difficulties in controlling for exposure to risk. However, evidence from the 23 papers reviewed (eight that examined intersections and 15 that examined straightaways) suggests that infrastructure influences injury and crash risk. Intersection studies focused mainly on roundabouts. They found that multi-lane roundabouts can significantly increase risk to bicyclists unless a separated cycle track is included in the design. Studies of straightaways grouped facilities into few categories, such that facilities with potentially different risks may have been classified within a single category. Results to date suggest that sidewalks and multi-use trails pose the highest risk, major roads are more hazardous than minor roads, and the presence of bicycle facilities (e.g. on-road bike routes, on-road marked bike lanes, and off-road bike paths) was associated with the lowest risk. CONCLUSION: Evidence is beginning to accumulate that purpose-built bicycle-specific facilities reduce crashes and injuries among cyclists, providing the basis for initial transportation engineering guidelines for cyclist safety. Street lighting, paved surfaces, and low-angled grades are additional factors that appear to improve cyclist safety. Future research examining a greater variety of infrastructure would allow development of more detailed guidelines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.366
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it