Market demand analysis and forecasting through applied econometrics: Empirical research in the public transit sector in the Toronto City Area (1969-2019)
Notice bibliographique
Résumé
Context: This working paper shall focus on sensitivity analysis, modelling, and forecasting of market demand to cope with uncertainty in the decision-making process. In addition, this paper also aims to provide the reader with an intuitive and practical experience of the optimisation tools of microeconomics, coupled with time series econometrics methods applied with R software. The strong interest in data from the public transit sector in this empirical research is linked to the fact that it is undoubtedly one of the sectors likely to play an important role in achieving the Sustainable Development Goals (SDGs) in future smart cities, which is all the more interesting as Toronto is Canada's largest municipality in terms of population. Indeed, public transit is a service that is beneficial to society for all its positive externalities. These benefits include accessibility and social equity in terms of fares and reduced road congestion, and, by extension, the fight against atmospheric pollution by reducing riders' CO2 emissions through public transport. Findings. (i) We shall provide a price-demand sensitivity model (fare in C$) for ridership demand inside the Toronto city area from 1969 to 2019 as well as the derivation of short- and long-term elasticities. We shall also provide, through modelling and forecasting, insight into future demand trends considering pre- and post-pandemic scenarios. It is clear that one of the most remarkable points to emerge from the transit data in recent times is that the effects of the pandemic crisis have slowed ridership demand by almost half (225 million riders in 2020) compared to the previous trend (525.5 million riders in 2019) in the City of Toronto (TTC operating service area only), causing a structural break in the time series data, also observed in the TTC's latest report (only 197.8 million riders in 2021). This is not an isolated case for the city of Toronto, as Montreal (only 200 million users in 2020 compared to 426 million in 2019) and most of the major metropolitan cities in the world that were affected also experienced the same slowdown in demand due to reduced activity during the pandemic. (ii) Using our estimated market demand model for transit in the city of Toronto, we will attempt to estimate the optimal price and the price at which consumers are no longer willing to pay. And since this study concerns a public transit company in the form of a natural monopoly commonly referred to as a state monopoly, we shall draw on the seminal work and contributions of Frank Ramsey and Marcel Boiteux (Ramsey-Boiteux pricing rule) to understand the optimal pricing behaviour of this type of network infrastructure. The aim of this research will therefore also be to understand, beyond the elements of microeconomic optimization, that the nature of the company is at the center of its interests, which has a considerable influence on certain aspects that determine its business model and in particular its pricing behaviour (Baumol, 1959). Keywords: market demand, sensitivity analysis, time series modeling and forecasting, pricing behaviour. JEL Classification: C22; C63; C87; D12; D22; D42
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».