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Record W2298786060 · doi:10.14288/1.0097639

Noise impacts of Automated Light Rail Transit in the Broadway and Nanaimo station areas of Vancouver

2010· article· en· W2298786060 on OpenAlexaboutno aff
Hugh Dundas McLean

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsLight rail transitNoise (video)Light railTransit (satellite)Transport engineeringGeographyAeronauticsTelecommunicationsComputer scienceEngineeringArtificial intelligencePublic transport

Abstract

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This thesis analyzes the impact of wayside noise produced by Automated Light Rail Transit (ALRT) in the Broadway Station and Nanaimo Station Areas of Vancouver. The hypothesis is divided into three sections. First, a semi-logarithmic relationship between ALRT noise and the distance from the guideway is anticipated, yet at the same distance at different points in the study area, noise levels can vary markedly. Second, in the area where ALRT noise levels exceed accepted standards, residents' perceptions are expected to be consistent with the measured impact. Third, at greater distances from the facility where the noise is acceptable, perceptions are anticipated to be inconsistent with the measured noise. The purposes of this thesis are to examine the relationship between noise levels and distance from the ALRT guideway, to define the zones of high and low noise impact, and to analyze residents' perceptions of ALRT noise based upon the measured noise level within each zone of impact. Primary data for this thesis came from three separate sources. ALRT noise levels forecast for 1986 were obtained from a consultant's report prepared for B.C. Transit in 1983. The East Vancouver Neighbourhoods Study surveyed residents in the Broadway and Nanaimo Station Areas, and elsewhere, during construction of the ALRT in 1984. In April 1986, measurements of wayside ALRT noise and a survey of residents were undertaken by the UBC transportation planning students. A 24-hour energy-equivalent level (L eq) was calculated separately for background noise and for wayside ALRT noise. The total 24-hour L eq was calculated by combining these two L eq. The relationship between noise and distance was then computed using regression analysis. Where applicable, an adjustment was made to the L eq based on established criteria for previous community exposure and background noise, in order to define the zones of impact. The zone of high impact was defined as the area in which noise levels are higher than acceptable, a 24-hour L eq of 55 dB or more. Perceptions of ALRT noise and neighbourhood noise were analyzed in relation to the adjusted L eq and socio-economic variables. A pre-ALRT outlook on the ALRT's influence on neighbourhood noise was analyzed in terms of the anticipated zones of impact, and socio-economic variables. The relationship of noise and distance is semi-logarithmic. Given the same distance from the ALRT guideway, noise levels vary noticeably at different points between the two Stations. The zone of high impact ranges from 50 to 200 feet from the ALRT guideway. In the high-impact zone, the perceptions toward ALRT noise and neighbourhood noise are consistent with the measured noise (24-hour L eq). However, perceptions of noise in the zone of low impact do not appear to be consistent with the measured noise levels. In the pre-ALRT study, residents in the high-impact zone tended to have a neutral outlook on anticipated ALRT noise levels. In the low-impact zone, negative perceptions toward ALRT noise appear to be related to a negative perception of traffic noise.

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)0.001
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 score0.810
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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
Published2010
Admission routes1
Has abstractyes

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