MétaCan
Menu
Retour à la cohorte
Enregistrement W2286806128 · doi:10.14288/1.0102349

Long range forecasting of domestic and international boarding pasengers at Canada airports by multiple regression analysis

2011· article· en· W2286806128 sur OpenAlexaboutno aff
Ronald Kenneth Gamey

Notice bibliographique

RevuecIRcle (University of British Columbia) · 2011
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueAviation Industry Analysis and Trends
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRange (aeronautics)Regression analysisGeographyEconometricsStatisticsEconomicsEngineeringMathematics

Résumé

récupéré en direct d'OpenAlex

The purpose of this thesis is to attempt to explain the forces behind the past growth of Canadian air travel and to use the explanation as a basis for forecasting the long-run growth of Canadian air travel. The forecasting attitude adopted in this study is that of the Department of Transport wishing to quantitatively forecast, to 1975, total Canadian domestic and international air passenger boardings independent of other modes, on the basis of average total Canadian data. Accurate forecasts are important to the Department of Transport since new airports cannot be constructed instantaneously, but at the same time, premature construction of airports is undesirable. There are a great variety of forecasting methods. Due to the problems of inadequate Canadian air passenger travel data, however, the author felt that the only appropriate quantitative method of forecasting air passenger boardings at the major Canadian airports, would be with dynamic and static, multiple regression models. The dynamic model is a new approach at forecasting air passenger boardings, since at the time of this study, not one example of its use in forecasting air passenger boardings could be found. The dynamic model of this thesis expresses the idea that current decisions are influenced by past behavior i.e. habit formation. Also, although there are many examples of the use of a static model for forecasting air passengers, the form of this study's static models is quite unique since it tries to take into account the increasing air travel elasticity of rising per capita incomes. There are many factors affecting demand but it was not possible to provide explicitely in multiple regression forecasting formulas for all of them because of the complexities involved and the lack of data with respect to some of them. It was found that one of the major factors affecting future boardings per capita will be fare policy. The long-run fare elasticity was found to be approximately -2.30. In forecasting air passenger boardings, five different assumptions were made with respect to future fare levels. The growth patterns of each of this thesis's five air passenger boarding forecasts based on the five future fare assumptions had two things in common: (1) all showed a declining rate of growth both in terms of boardings per capita and total Canadian boardings and (2) all showed absolute annual increments which in general increased from year to year throughout the entire forecast period. These two trends are both major characteristics of a growth industry which has not yet matured. An average annual decrease of 0.1334 current cents in the air passenger yield per passenger-mile seems the most reasonable future fare assumption. If this is so, the growth of total air passenger boardings will progressively decline from a 7.81 percent increase in 1968 to a 6.54 percent increase in 1975 and the growth of boardings per capita will progressively decline from a 5.07 percent increase in 1968 to a 4.35 percent increase in 1975. This forecasted growth is much lower than in the historical period of 1955-1966 when the average percent growth in total boardings was 11.4 percent and in boardings per capita was 8.48 percent. Of course, national forecasts of total domestic and international air passenger boardings are of little value in comparison to air passenger boarding forecasts of individual Canadian cities. Fortunately, the largest twenty-five air transportation hubs, which have accounted for 89 percent to 93 percent of the total of all Canadian air passenger boardings in the past, have through time each maintained a generally consistent relationship to the national total. Thus, by fitting numerous least-squares trend curves through each community's past percentage of national air passenger boardings and modifying where necessary because of the advice of experienced people in Canadian air travel, forecasted percentages of total Canadian boardings were arrived at for each of the largest twenty-five Canadian air transportation hubs.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,193
Score d'incertitude au seuil0,388

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,028
Tête enseignante GPT0,169
Écart entre enseignants0,141 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuecIRcle (University of British Columbia)Même sujetAviation Industry Analysis and TrendsTravaux en français237 207