East Coast Urbanites vs West Coast hippies – understanding differences in attitudes towards public transport
Notice bibliographique
Résumé
Hybrid model structures are becoming more common to integrate choice models with other model components to understand the role of attitudes and perceptions in choices beyond the standard variables of time, cost, frequency, etc. We believe these hybrid models are important, as it has been demonstrated that taste heterogeneity is influenced by factors beyond simply operational variables for a product or service. However, applications for real world work (as opposed to academic research) are still relatively limited and many applications fail to provide convincing insights for policy makers. This paper provides a large scale application of ICLV models in a context where two groups of respondents from different areas and with different contexts are faced with similar choices. From a policy perspective, there is growing interest in encouraging a shift away from private cars to public transport in the United States, where the uptake remains low compared to almost all other developed nations. We specifically look at both East Coast and West Coast respondents, meaning that differences arise in terms of geography, experiences, culture, and socio-demographics. There are known and clear a priori segments in our study. Namely, the differences between Northeast Corridor (NEC, Boston to Washington DC through NYC) respondents, who live in an established urban “megaregion” and have access to intercity rail service akin to European rail service, as well as significant bus and air service. The options for intercity travel in the Northeast Corridor Amtrak are more robust than anywhere else in the US, and the rail service carries more passengers per day than competing air service, with about 40 trains per day in each direction and a top speed of 150 MPH. This is rare in the US. The culture in the highly populated NEC is urban and sophisticated and located in the oldest “settled” part of the US with three of the US’s largest and oldest cities (Boston, New York, and Washington, DC). This is where the “establishment” of the US is located (as the politicians say). Meanwhile, the Cascade Corridor respondents (Vancouver BC to Portland, OR, through Seattle) live in a more typical American intercity corridor from a transportation perspective, with smaller cities and just a few intercity mode options. The cascade corridor’s intercity mode share is also more typical for America, with low rail market share for an infrequent train service of 5 trains per day in each direction and a top speed of 79 MPH, with much higher shares for air and auto modes, as well as some bus service. The culture in the pacific northwest is also quite different from the northeast. It still has a frontier/alternative feel and overall has a more outdoorsy/alternative culture, with arguably some of the best (if not the best) beer and coffee in the world, which is important due to low numbers of sunny day in the region. A comparison of results across generic and area specific models will allow us to answer not just questions in terms of differences across areas, but also whether these differences are intuitive, relate to socio-demographic, infrastructure, or attitudes.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».