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Record W2109648075 · doi:10.1002/atr.104

Severity of urban transit bus crashes in Bangladesh

2010· article· en· W2109648075 on OpenAlexafffundvenue
Upal Barua, Richard Tay

Bibliographic record

VenueJournal of Advanced Transportation · 2010
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of Calgary
FundersAccident Research Centre, Monash UniversityAlberta Motor Association Foundation for Traffic SafetyCentre for Transportation Engineering and Planning
KeywordsCrashTransport engineeringPedestrianTransit (satellite)Probit modelOrdered probitProbitCollisionPoison controlDeveloping countryBusinessComputer scienceGeographyPublic transportEngineeringEnvironmental healthComputer securityEconomic growthMedicineEconomics

Abstract

fetched live from OpenAlex

Abstract Unlike in developed countries where buses are a relatively safe mode of transport, there is a significant safety concern in many developing countries like Bangladesh regarding transit buses. Nevertheless, few studies have examined the factors contributing to the number or severity of bus crashes. Using the ordered probit model on bus crash data from 1998 to 2005 in Dhaka, Bangladesh, our study shows that there is a general increase in the severity of transit bus crashes over this period. Also, crash severity tends to increase when the collision occurs on weekends, off‐peak periods, and two‐way streets or involves only one vehicle, a pedestrian, and other vulnerable road users. On the other hand, the severity of a crash tends to be lower at locations with some form of police control or road medians, as well as for crashes involving hit object, parked vehicles, or sideswipes. Copyright © 2010 John Wiley & Sons, Ltd.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.004
GPT teacher head0.199
Teacher spread0.196 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations107
Published2010
Admission routes3
Has abstractyes

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