An Examination of Under-studied Aspects of Ride-sourcing Adoption and Use in Large Metropolitan Areas
Notice bibliographique
Résumé
Since they first entered the market in 2009, ride-sourcing has experienced significant growth in both popularity and utilization. The growing prevalence of ride-sourcing resulted in significant effort being dedicated to understanding the use and impacts of these services, which have highlighted the potential benefits and negative externalities associated with ride-sourcing. Consequently, understanding ride-sourcing adoption and use is crucial to mitigate the negative externalities and capitalize on the benefits associated with these services. This dissertation aims to expand on the current understanding of ride-sourcing adoption and use by building on the findings of studies published in the decade following the introduction of ride-sourcing. In pursuit of this goal, this dissertation examined aspects of ride-sourcing use that have received relatively little attention in the literature and explored different approaches for incorporating ride-sourcing into mode choice models. To help accomplish these objectives, web-based surveys were designed and conducted to collect information on ride-sourcing adoption and use in Toronto, the Greater Toronto Area, and Metro Vancouver. Using this data, statistical models and advanced econometric models were estimated to gain insights into ride-sourcing adoption and use. Examples include the estimation of a two-stage multinomial logistic regression model to understand the determinants of ride-sourcing adoption and user profile membership and the estimation of two-stage ordered generalized extreme value models to understand the determinants of anticipated post-pandemic ride-sourcing use. Additionally, error component mixed logit models and a joint RP-SP model were estimated and applied to explore the influence of ride-sourcing on the demand for existing modes. Besides, household travel survey data was used to explore different approaches for incorporating ride-sourcing into mode choice models. The findings presented in this dissertation can help inform efforts to mitigate the negative externalities associated with ride-sourcing and offer insights into the potential benefits of these services. Specifically, the results can contribute to efforts to encourage shared ride-sourcing use as well as initiatives to use on-demand services to serve areas where fixed-route transit may not feasible. Additionally, the results underscore the potential for post-pandemic ride-sourcing use to differ from that of pre-pandemic use, which could have important transportation planning implications.
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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 | 0,005 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».