Towards a better implementation of accessibility indicators in land use and transport planning practice
Notice bibliographique
Résumé
In order to address the shortcomings of mobility-centered transport planning, planning for accessibility is increasingly considered as an essential complementary approach. This approach aims to provide reasonable access to destinations (employment centers, amenities, social and health services, etc.) to the entire population instead of prioritizing the optimization of travel times. Although a large body of literature has explored how to conceptualize and measure accessibility, research on how to incorporate accessibility in transport planning is scarce. Knowing that accessibility is currently marginalized in transport planning, the overarching goal of this dissertation is to contribute to the implementation of accessibility measures in land use and transport planning practice, by answering the following research question: How can accessibility measures be incorporated into current land use and transport planning practice in order to improve our understanding of the performance of land use and transport systems?To answer this question, the following objectives will be pursued: 1. To determine how accessibility is used in land use and transport planning practice; 2. To identify appropriate measures of accessibility to be used in land use and transport planning practice; 3. To generate measures of accessibility in a data-challenging context in collaboration with local transport planners. To reach these objectives, this dissertation follows a manuscript-based approach, with four studies building on one another. Collectively, these manuscripts address both the planning and research realms of transport planning through a multifaceted approach.Through an analysis of 32 metropolitan transport plans around the world, the first study reveals that, while the concept of accessibility is considered in most planning documents, it is rarely translated into goals and indicators that reflect the ease of reaching destinations. The findings of the first study are strengthened by a second study surveying 343 practitioners about accessibility. The results of the study demonstrate that most practitioners, although aware of the concept of accessibility, do not consider the ease of reaching destinations in their work. In addition, the results identify two main barriers to the implementation of accessibility indicators: lack of knowledge and lack of data. In light of the knowledge and data barriers, the third study assesses the usability of various accessibility measures from a planning perspective. Three measures of accessibility to jobs by public transport in the Greater Toronto and Hamilton Region are generated and assessed through a mode share regression model. The study concludes that the simplest measure – the number of jobs that can be reached within 45 minutes of travel at 8 am – is the most adequate to assess the performance of land use and transport systems at the regional level. Using the measure identified in the above study, the last study conducts an equity assessment of public transport services in four large metropolitan areas in Brazil. The study, led in a data-challenging context, proposes a methodology that can be easily applied by any transport agencies and illustrates the relevance of the accessibility indicators to inform planning processes. Conducted in collaboration with local transport planners, the study also contributes to an enhanced collaboration between research and planning. Overall, this dissertation presents a set of complementary studies to bridge the gap between research and practice and better understand how accessibility indicators can be incorporated into current land use and transport planning practice. This dissertation demonstrates the importance of carefully and critically thinking about how to include accessibility indicators in practice, be it with respect to how it is defined or how it is measured, and about how research can better contribute to the current challenges faced by professionals.
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 | 0,128 | 0,243 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,010 | 0,016 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,021 | 0,022 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».