Modeling bicyclists' destination location choice: Spatial-temporal constraint for choice set generation
Bibliographic record
Abstract
With growing environmental concerns and increased awareness about physical fitness, many people are shifting their travel mode to bike for various activities. Hence, understanding bike users' destination choice behavior is crucial for developing policies and making infrastructure investment decisions. This study develops a mixed logit model with panel effect to analyze destination location choice for bike user’ home-based tours. The study adopts time-space prism concept to generate activity-specific choice sets, accommodating spatial, temporal, and physical strength constraints. The model is estimated using travel survey data from the Okanagan region of British Columbia. It has been tested for different sizes of choice sets, and goodness-of-fit measures have been compared to identify the model that best fits the data. The results suggest that tour-related attributes, individual characteristics, work profiles, built environment characteristics, and transportation infrastructure-related variables significantly influence destination choices. For example, bike users prefer destinations closer to home. However, variation exists based on tour types. For simple tours with one destination, bike user may travel longer distances, whereas for complex tours with multiple destinations, they may prefer shorter distances for each leg of the tour. Telecommuters may travel longer distances, while commuters show significant variability in their destination choices – some prefer traveling shorter distances while others travel farther. Important policy variables include a higher density of activity destinations, bike lane to road length ratio, and bike index, which may attract more biker users. The findings of this study will facilitate in developing travel demand models sensitive to bike users' travel behavior.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".