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Record W2321926896 · doi:10.1061/9780784413036.206

Effects of Bus Parking Positions on Waiting People's Safety at Bus Station

2013· article· en· W2321926896 on OpenAlexaff
Xiaofeng Ma, Jinshan Shi, Wei Zhang, Peng Chen, Yaqiu Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsMinistry of Transportation of Ontario
FundersNational Natural Science Foundation of China
KeywordsTransport engineeringSchool busAutomotive engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Accidents usually occur at bus stations, especially during peak hours. In China, there are usually more than 30 persons waiting for buses at the same time, and they often stand on the roadway instead of in the waiting area. This study attempts to determine the parking patterns of bus drivers when they are pulling into bus stations. The travelers' behavior at bus stations when they are waiting for buses is recorded by a videotape recorder. It is assumed that the parking position of buses is influenced by the security level of the bus stations. So, experiments were carried out on 10 groups and got 116 samples about harbor-shaped and non-harbor-shaped bus stations. According to the results, it was found that the travelers' waiting habits and cars parking in the bus station area are the main reasons for passenger-vehicle conflicts. However, the bus parking positions are not correlated with conflicts, which is the opposite of what was expected.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.002
GPT teacher head0.164
Teacher spread0.161 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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