If you build it, who will come? Exploring the effects of rapid transit on residential movements in Metro Vancouver
Bibliographic record
Abstract
As cities across the world embrace the benefits of rapid transit technology and invest in the expansion of existing infrastructure or plan for the introduction of new lines, the differences in both benefits and externalities that bus rapid transit (BRT) and rail rapid transit (RRT) bring remain unclear. This study aimed to address that gap and understand whether there was a distinction in impacts on the residential migration of households in different income and residential tenure groups as the result of BRT and RRT projects. This was achieved by exploring the effects of both modes in the same metropolitan region—metro Vancouver. This study used three BRT and three RRT lines that were in service for all or part of the 20 years spanning 1996 through 2016 to assess the rates of in-movement of households by income in Census Tracts (CTs) within 800 meters (½-mile) of a given rapid line. Our analysis suggested that areas adjacent to the Expo-Millennium RRT Corridor saw fewer in-movers between the 2001 Census and the 2016 Census than the areas without rapid transit infrastructure, while the same was true for the CTs affected by BRT lines and that had a larger than average share of new housing while holding everything else (e.g., housing supply) constant. While we did not find evidence to state that the presence of rapid transit infrastructure disproportionately affected any one of the income groups, our analysis suggested that there were more affluent renters moving in along the RRT and BRT lines. At the same time, the share of low-income renters that moved into areas close to rapid transit lines remained relatively stable. This research added a unique perspective to the debate cities and transport agencies have been experiencing with respect to decisions around the investment into different transport technologies and contributed to the argument for the need to carefully plan and provide rapid transit infrastructure together with affordable and diverse housing options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".