Existence and Use of Low-Pollution Route Options for Observed Bicycling Trips
Bibliographic record
Abstract
Do routes with lower doses of air pollution exist in real-world bicycling networks, and do bicyclists actually use those routes? Low-pollution-dose alternative routes for a sample of urban cycling trips were modeled and compared with shortest paths. Bicyclists’ actual route choices on the same trips were observed with the use of GPS data and compared with the low-dose and shortest paths alternatives. With use of past studies of pollution exposure levels and simplified ventilation rates, link-inhaled doses of air pollution were estimated. Findings suggest that a majority of trips have lower-dose alternatives to the shortest path, with a 12% average dose reduction. Cyclists tend to choose routes with pollution concentrations between those of shortest paths and minimum-dose routes, but they also travel considerably farther, leading to total inhaled doses that are higher than on either alternative route. People’s seeming avoidance of nontraffic factors such as hills, excess turns, and difficult intersections leads to longer than optimal detours from a pollution avoidance perspective. Bike paths and bike boulevards (traffic-calmed streets with bicycle priority), as well as denser street grids, appear to provide effective low-pollution alternatives, although such routes tend to encourage excess detours that can add to total inhaled dose. Bike lanes can draw cyclists onto more polluted routes in some circumstances, with poor pollution inhalation outcomes. Overall, excess doses did seem to be a common problem for this sample of cyclists on a real-world network. The study’s findings support policies that provide dense networks of attractive facilities that encourage cyclists to choose direct, lower-pollution routes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".