Employment relocation, residential preference, and transportation mode choice: the case of the Justice Institute of BC [sic]
Notice bibliographique
Résumé
Over the last 100 years technological improvements in urban travel in terms of reliability and speed, has meant increased mobility for residents. This was accelerated with the advent of the automobile. It allowed many to move to the suburbs that were typified by less expensive lower density housing, and commute longer distances to their place of work. Today, in urban areas, cars are the main means of urban transport. The problem arises in major urban areas across North American when everyone tries to travel at the same time (usually during to trip to and from work). Urban areas are faced with problems of congestion (during rush hour) along with the lack of attractive transit alternatives. One aspect of this problem is examined in terms commuting habits. The purpose of this exercise is to examine the commuting habits of Justice Institute employees whose place of work moves from the West Side of Vancouver to New Westminister. In the postmove period employees made a number of decisions regarding their modal-type and residential location. These decisions may have a significant impact on their activities and travel patterns in the city. The goal is to collect data that would indicate the place of residence of employees before and after the Justice Institute move. It should also include employee modal-type in the pre and postmove periods of the move. Such information is important in the understanding the changes' employees make regarding their residential location and modal-type and the reasons for these changes. As well, employee characteristics such as income can influence these decisions. Such decisions are based on employee's preferences, likes and dislikes regarding their neighbourhood and modal-type. Within this framework, it is the goal of this analysis to understand how employees make trade-offs between where they live and the time they spend commuting to and from work. The correlation parameter may describe the tendency for some commuters to locate themselves close to their employment. The analysis of the survey results will help planners understand more about the urban transport problem. Within this framework, planners can learn why people choose to travel by car instead of transit. This may be related to choice of neighbourhood. It may be that employees choose neighbourhoods that they like to live in regardless of their place of work. Thus, to understand more about the transport problem planners need to know what kinds of neighbourhoods attract people. If the quality of neighbourhoods is an important factor regarding employees' choice of residential location, any transport plan must include land-use initiatives that attempt to create neighbourhoods that attract people. The idea is to bridge the two; otherwise conflicting land-use policies could easily undermine any transport plan. Within this framework, policy must be geared to bring home and places of work closer together. This means creating vibrant neighbourhoods that contain a variety of land-use that could create more employment opportunities closer to home. Neighbourhoods should not only create just residential uses alone. That would mean people would have less distance to travel. This would also mean creating pedestrian and transit friendly neighbourhoods. Less emphasis would be given to the car and more to alternative methods of transport. Such policies can go along way in reducing the dependence on the car.
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 | 0,001 |
| 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 | 0,007 | 0,001 |
| Communication savante | 0,002 | 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) | 0,006 | 0,001 |
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 ».