Uniquely Satisfied: Exploring Cyclist Trip Satisfaction
Bibliographic record
Abstract
Despite increasing interest and focus on cycling planning and infrastructure, many research and policy frameworks overlook two important aspects of cycling: motivations and trip satisfaction. While many studies have found that cyclists are more satisfied with their commute than other mode users, few have explored why. The authors hypothesize that different types of cyclists—defined by their reasons for cycling and seasonal mode patterns—will derive different levels of satisfaction from cycling. Therefore, this study attempts to 1) examine the effect of built environment characteristics (e.g. intersection density, land use), trip characteristics (e.g. distance and slope) and season on cycling trip satisfaction, 2) group respondents into 'cyclist types' based on a cluster analysis of motivations for cycling and their alternate (winter) mode, and 3) understand how these personal characteristics moderate the relationship between built environment and trip characteristics and expressed trip satisfaction. This is accomplished using a university-wide travel survey administered in winter 2011, in which commutes to McGill University were asked to report their last trip to McGill. If the person uses a different mode during the fall he was asked to report it as well. Individuals were also asked to report their level of satisfaction with these trips. Surprisingly, the expected relationship between distance, slope and objectively measured elements of the built environment and trip satisfaction was not found. Similar to previous research, cyclists are found to be more satisfied with their commute than other mode users. Year-round cyclists are less satisfied with their travel than those who only cycle in good weather; while Cycling Enthusiasts are significantly more satisfied than most cyclists motivated by convenience. This work emphasizes the need to look beyond the built environment and trip characteristics to better understand cyclist trip satisfaction.
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".