How Houston can prioritize pedestrians over vehicles with the strategic removal of freeways
Bibliographic record
Abstract
Houston is a city that heavily relies on its freeways for transportation and development. However, these freeway structures are approaching their expiration dates, and the City of Houston must decide on viable transportation methods moving forward. Will Houston choose sustainable transportation solutions that promote pedestrians over vehicles or choose traditional costly methods of maintaining and expanding freeway infrastructure? This report seeks to take the lessons learned from case studies and apply them to sustainable urban design solutions for Houston. This report is timely as Houston recently proposed a controversial North Houston Highway Improvement Project (NHHIP) which calls for the action of expanding portions of its freeways from downtown to the outer edge of the city. This report focuses on 11 case studies of cities who have removed or are considering the removal of their freeway structures. The five case studies I looked at in the United States were The Central Freeway in San Francisco, The Park East Freeway in Milwaukee, The Harbor Drive in Portland, The I-490 Inner Loop in Rochester, The Alaskan Way Viaduct in Seattle. The case studies in other countries were the Georges Pompidou Expressway in Paris and the Cheonggye Freeway Seoul. The case studies considering removing infrastructure were BQE Expressway in New York, The Claiborne Expressway in New Orleans, I-35 in Austin, and The Gardiner Expressway in Toronto. All case studies were meticulously reviewed, and the results of the cities that chose to remove infrastructure were documented. It was found that cities who chose to remove freeways experienced benefits that revitalized their communities and boosted their economies. Freeway removals did lead to gentrified neighborhoods although policies like affordable housing were found to help temper displacement effects. Data from case studies also revealed that freeway removal did not appear to seriously sacrifice transportation performance.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".