Effects of Perceived Traffic Risks, Noise, and Exhaust Smells on Bicyclist Behaviour: An Economic Evaluation
Bibliographic record
Abstract
Active mode (walking, bicycling, and their variants) users are exposed to various negative externalities from motor vehicle traffic, including injury risks, noise, and air pollutants. This directly harms the users of these modes and discourages their use, creating a self-reinforcing cycle of less active travel, more motorized travel, and more harmful effects. These impacts are widely recognized but seldom quantified. This study evaluates these impacts and their consequences by measuring the additional distances that bicyclists travel in order to avoid roads with heavy motor vehicle traffic, based on a sample of German-Austrian bicycle organization members (n = 491), and monetizes the incremental costs. The results indicate that survey respondents cycle an average 6.4% longer distances to avoid traffic impacts, including injury risks, air, and noise pollution. Using standard monetization methods, these detours are estimated to impose private costs of at least €0.24/cycle-km, plus increased external costs when travellers shift from non-motorized to motorized modes. Conventional transport planning tends to overlook these impacts, resulting in overinvestment in roadway expansions and underinvestments in other types of transport improvements, including sidewalks, crosswalks, bikelanes, paths, traffic calming, and speed reductions. These insights should have importance for transport planning and economics.
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 | 0.002 | 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 | 0.000 |
| 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".