Methods of Determining and Predicating Community Annoyance due to Aircraft Noise
Bibliographic record
Abstract
As the importance of the aviation industry continues to grow, airports continue to expand, andincrease operations; noise generated from aircraft is becoming a growing concern to people livingwithin the communities surrounding the airports. Community members have shown concern throughcomplaints and annoyance due to the disturbances that aircraft noise cause in their everyday life. Thepurpose of this study is to determine which factors most affect community annoyance towards aircraftnoise. There are two main factors to be considered when analyzing annoyance due to aircraft noise.The first is the acoustic factors caused by the aircrafts themselves, such as the noise the aircraft emitsor the frequency of flights. Second are the non-acoustic factors such as the attitudes and views anindividual may have towards the local airport or aircraft in general. Additional examples include thetime of disturbances or how the aircraft makes the individual feel when they hear or see one. Althoughit can be argued that the number of complaints aimed towards aircraft noise and airports is a goodindication of the impact they have on a community, it is not a good indication of annoyance or thetrue effect on the community as a whole, given for example, that many complaints can be repeatedlysent from one or only a few individuals. A community annoyance survey has the advantage of beingable to include a large body of the community to gauge the effect of aircraft noise and to determinewhich factors contribute most to annoyance. For this research, the communities around TorontoPearson International Airport were studied. A noise annoyance survey containing 3 sections and atotal of 35 questions was sent out to 31 regions around the Greater Toronto Area, 25 of which werenear to permanent airport noise monitoring stations. The survey responses were analyzed todetermine the percentage of highly annoyed (%HA) individuals and the factors that contributed mostto the highly annoyed (HA) individuals. Furthermore, the onset of the COVID-19 world pandemicpresented a unique opportunity to include impacts on community annoyance during a period ofsignificant reduction in airport operations and air traffic volume. As such, the noise annoyance surveyalso considered the annoyance experienced by community members during the start of COVID-19in order to better determine what factors affect community annoyance the most. Responses wereanalyzed and %HA was calculated, as well as the number of HA individuals using the responses tothe standardized ISO 15666 questions in order to determine the annoyance within thecommunity. %HA and number of HA individuals from ISO 15666 were then compared to themeasured noise levels and responses to questions from the annoyance survey to determine whichfactors most contribute to the annoyance of HA individuals.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".