A Problem Oriented Approach to Urban Transportation Planning
Bibliographic record
Abstract
The complexity behind the urban phenomenon and the relationship between its systems and agents make the integrated land use and transportation (ILUT) planning practice a challenging and sophisticated step of the decision-making process. The difficulty in defining an adequate representation of problems that reflects both the perception and interests of all stakeholders involved and the interaction of the land use and transportation systems has often resulted in opposition and backlash towards decisions. Moreover, it seems clear that ILUT planning should focus on accessibility, promoting the efficient movement and access for people in urban areas and fulfilling a supporting role in promoting social inclusion, economic opportunities, and overall quality of life. This research addresses the existing theoretical, conceptual, and methodological gaps regarding urban transportation planning. It focuses specifically on the early stages of the planning process, seeking to highlight the importance of diagnosis for planning and its relevance to the later definition of objectives. It aims to answer the following questions: How does current planning practice identify and diagnose problems? How to frame “diagnosis” within ILUT planning? How, within the accessibility paradigm, can accessibility measures help to diagnose problems and inform the decision-making process? The research presents a literature review that highlights conceptual and methodological gaps, sets the current paradigms in the urban transportation planning field, and discusses different methodological approaches to address the complexity and systematic nature of urban problems. It also introduces an overview of the current urban and transportation planning practice in the Greater Toronto and Hamilton Area through a survey of planning practitioners. Additionally, it expands the discussion on problems and proposes a problem-oriented planning framework to identify, characterize, and diagnose problems. A theoretical exercise is presented to illustrate how planning dilemmas could benefit from the proposed planning framework in reconciling conflicting interests within a future vision for the city. Lastly, a case study is developed to characterize accessibility problems under the equity lens in the City of Toronto, allowing for a better understanding of how the land use and transportation systems, as well as individuals attributes, interact in shaping the socio-spatial reality where problems may emerge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".