MODELLING THE FUTURE OF LAND USE, TRANSPORTATION AND THE ENVIRONMENT IN THE WINDSOR CMA, ONTARIO, CANADA
Bibliographic record
Abstract
The overall objective of this research is to examine the long-term impacts of alternative urban form scenarios on the sustainability of the transportation system using the Windsor CMA as a case study. The specific objectives of this research work are as follows: 1. Design several land use scenarios to identify potentially sustainable urban forms in the study area, 2. Predict the population and employment distributions associated with the designed land use scenarios for the period 2011 to 2031, 3. Model the scenarios from objectives 1 and 2 using traffic assignment and emission simulation software to obtain traffic and environmental impacts, and 4. Conduct a comparative analysis across scenarios to provide insights that could help inform the planning process in the studied city. The first part of the modeling exercise is to predict and distribute the population and employment growth in the Windsor CMA for four scenarios (status-quo, compact, sprawl and multinucleated scenarios). Multinomial Logit Modeling (MNL) and Multi-Criteria Evaluation (MCE) techniques were used to handle the land use development scenarios. The second part of this thesis is focused on examining the traffic performance under different urban form scenarios. More specifically, Vehicle Kilometers Traveled (VKT), Vehicle Minutes Traveled (VMT), energy consumption and harmful gas emissions (e.g. HC, CO and NOx) were calculated and analyzed. Results indicate that the sprawl pattern would contribute to or result in a non-sustainable future in the Windsor CMA. In comparison, compact development results in lower VKT, VMT and emissions over time. Consequently, the future planning process in the region could benefit from promoting a more compact urban form since the latter is likely to lead to a more sustainable urban future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".