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Record W2746405782 · doi:10.3390/ijerph14080930

Cycle Tracks and Parking Environments in China: Learning from College Students at Peking University

2017· article· en· W2746405782 on OpenAlexaboutno aff
Changzheng Yuan, Yangbo Sun, Jun Lv, Anne Lusk

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsTransport engineeringChinaBeijingPedestrianEngineeringGeography

Abstract

fetched live from OpenAlex

China has a historic system of wide cycle tracks, many of which are now encroached by cars, buses and bus stops. Even with these conditions, college students still bicycle. On campuses, students park their bikes on facilities ranging from kick-stand-plazas to caged sheds with racks, pumps and an attendant. In other countries, including Canada, some of the newer cycle tracks need to be wider to accommodate an increasing number of bicyclists. Other countries will also need to improve their bike parking, which includes garage-basement cages and two-tiered racks. China could provide lessons about cycle tracks and bike parking. This study applied the Maslow Transportation Level of Service (LOS) theory, i.e., for cycle tracks and bike parking, only after the basic needs of safety and security are met for both vehicle occupants and bicyclists can the higher needs of convenience and comfort be met. With random clustering, a self-administered questionnaire was collected from 410 students in six dormitory buildings at Peking University in Beijing and an environmental scan of bicycle parking conducted in school/office and living areas. Cycle tracks (1 = very safe/5 = very unsafe) shared with moving cars were most unsafe (mean = 4.6), followed by sharing with parked cars (4.1) or bus stop users (4.1) (p < 0.001). Close to half thought campus bike parking lacked order. The most suggested parking facilities were sheds, security (guard or camera), bicycle racks and bicycle parking services (pumps, etc.). If parking were improved, three quarters indicated they would bicycle more. While caged sheds were preferred, in living areas with 1597 parked bikes, caged sheds were only 74.4% occupied. For the future of China’s wide cycle tracks, perhaps a fence-separated bus lane beside a cycle track might be considered or, with China’s recent increase in bike riding, shared bikes and E-bikes, perhaps cars/buses could be banned from the wide cycle tracks. In other countries, a widened cycle track entrance should deter cars. Everywhere, bike parking sheds could be built and redesigned with painted lines to offer more space and order, similar to car parking.

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)0.002
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 score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0060.002
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.394
Teacher spread0.329 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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