Methods of Determining and Predicating Community Annoyance due to Aircraft Noise
Notice bibliographique
Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
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| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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