What is Generating Transit Ridership Increase in US and Canadian Cities
Bibliographic record
Abstract
Transit ridership is rising in America. Ridership in the United States (US) has increased by more than 37% since 1995, outpacing population growth of 20%, and increased by 1.1% in between 2012 and 2013, in spite of falling fuel prices. Transit has always been an important form of transport in some large American cities, like New York, Boston and San Francisco. But in the past decade, transit has been increasing in most US cities. Smaller cities like Flagstaff, Arizona and Canton, Ohio, and auto-centric ones like Los Angeles and Indianapolis, are seeing large gains. Canadian cities have seen even greater increases, in larger cities like Vancouver and Toronto as well as smaller ones like Regina, Alberta and Oakville, Ontario. The authors, who have performed transit system restructuring projects in a number of smaller and mid-sized US and Canadian cities, document the long-term demographic and socio-economic trends that are the underlying cause of this ridership growth, and explain why it is likely to continue. These trends include the changing tastes and lifestyle preferences of the millennial generation, the retirement from work of the baby boom generation, concerns about environmental issues and global climate change, growing economic polarization, immigration, and changes in the population’s ethnic composition. Many of these same trends will affect European cities, albeit in ways that are unique to each city and country, and subtly different from most American cities. In many cities and regions, the funding structures that have supported local transit systems are inadequate to meet the growing demand for transit service. Likewise, the systems that national governments use to support transit infrastructure development are generally inadequate to meet the demand for vehicles and infrastructure that these long term trends will generate. Policymakers both in North America and Europe will soon be forced by public demand to allocate greater importance, and greater funding, to transit to meet this growing demand.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".