Notice bibliographique
Résumé
A recurring issue is that of increased car dependence in major North American cities. Policy makers are challenged to find new and innovative solutions to counter the negative externalities of this personal vehicle dependence. For instance, the air pollution and greenhouse gas emissions resulting from private vehicular travel is of a particular concern for the health and safety of future generations. Moreover, the prevalence of sub-urban life in North American cities in the recent years has resulted in increased private vehicle usage while reducing public transportation systems usage. A well planned and efficient public transportation system can provide equitable service and accessibility to the population as well as contributing to the reduction of air pollution and GHG emissions. An effective solution lies in transit agencies and government implementing policies that maximize transit use and minimize car dependence. Not surprisingly, many urban regions are enhancing public transportation infrastructure to address the private vehicle use challenge. A number of research efforts have been focussing on understanding individual behavioral challenges in using transit while several other studies have examined the factors affecting transit operations. These studies provide important information to local agencies and transit agencies to enhance public transit services and operations. This thesis is a collection of three distinct studies, each relating to public transportation issues from different perspectives. The first study examines individual home to work/school commute patterns in Montreal, Canada with an emphasis on the transit mode of travel. The overarching theme of this research is to examine the effect of the performance of the public transportation system on commuter travel mode and transit route choice (for transit riders) in Montreal. We investigate two specific aspects of commute mode choice: (1) the factors that dissuade individuals from commuting by public transit and (2) the attributes that influence transit route choice decisions (for those individuals who commute by public transit). The second study is an effort to develop a framework for a better understanding of commuter train users' mode and station choice behavior. Typically, mode and station choice for commuter train users is modeled as a hierarchical choice with mode being considered as the first choice in the sequence. This research proposes a latent segmentation based approach to relax the hierarchy. In particular, this innovative approach simultaneously considers two segments of station and access mode choice behavior: Segment 1 - station first and mode second and Segment 2 – mode first and station second. The allocation to the two segments is achieved through a latent segmentation approach that determines the probability of assigning the individual to either of these segments as a function of socio-demographic variables, level of service (LOS) parameters, trip characteristics, land-use and built environment factors, and station characteristics. Finally, the third study draws attention to the spatial characteristics affecting transit ridership. An analysis of bus stop level boarding and alighting is undertaken by developing ordered response models of the bus stop specific boarding and alighting by time of day. The analysis quantifies the influence of various exogenous factors including public transit accessibility indices (number of bus/metro/train stops around each stop, length of bus/metro/train lines, length of exclusive bus lanes), infrastructure attributes (road length by functional classification, bike lane lengths, distance to central business district, CBD), and land use measures (number of parks and their areas, residential area, number of commerces and their area, government and institutional area, resource and industrial area, and population density).
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 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 ».