Analysis of Bicycling Trends and Policies in Large North American Cities: Lessons for New York
Bibliographic record
Abstract
This research report reviews trends in cycling levels, safety, and policies in large North American cities over the past two decades. The authors analyze aggregate national data as well as city-specific case study data for nine large cities (Chicago, Minneapolis, Montreal, New York, Portland San Francisco, Toronto, Vancouver, and Washington). The number of bike commuters in the USA rose by 64% from 1990 to 2009, and the bike share of commuters rose from 0.4% to 0.6%. Over the shorter period from 1996 to 2006, the number of bike commuters in Canada rose by 42%, and the bike share of commuters rose from 1.1% to 1.3%. From 1988 to 2008, cycling fatalities fell by 66% in Canada and by 21% in the USA; serious injuries fell by 40% in Canada and by 31% in the USA. Cycling rates have risen much faster in the nine case study cities than in their countries as a whole, at least doubling in all the cities since 1990. The case study cities have implemented a wide range of infrastructure and programs to promote cycling and increase cycling safety: expanded and improved bike lanes and paths, traffic calming, parking, bike-transit integration, bike sharing, training programs, and promotional events. The authors describe the specific accomplishments of the nine case study cities, focusing on each city's innovations and lessons for other cities trying to increase cycling. Although cycling has almost doubled in New York City since 1990, it lags far behind the other case study cities in almost every respect. It has the lowest bike share of commuters, the highest cyclist fatality and injury rate, and the lowest rate of cycling by women, children, and seniors. New York has built the most bikeways of any North American city since 2000 and has been especially innovative in its use of cycle tracks, buffered bike lanes, bike traffic signals, bike boxes, and sharrowed streets. Yet New York has almost completely failed in the important areas of bike-transit integration and cyclist rights and falls far short on bike parking and cycling training. Moreover, the refusal of New York's police to protect bike lanes from blockage by motor vehicles has compromised cyclist safety. New York has much to learn from the other case study cities, which have implemented a far more comprehensive, integrated package of mutually reinforcing policies to promote cycling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".