Bibliographic record
Abstract
Non-work-related trips comprise a considerable percent of total trips, but there are a limited number of studies that investigate travel behavior related to these trips. This study investigates non-work-related travel behavior in two parts. The first part examines mode choice behavior, while the second part analyzes departure time choice. A Random Regret-Minimization (RRM) approach is applied on stated preference data that was collected in Calgary, Canada. We study mode choice behavior by examining the impacts of various sustainable neighborhood design elements like availability of vegetation/trees or shelter, speed and number of other vehicles on the streets, and the number of major intersections to cross on mode choice. We explore the impact of temperature on the non-work-related trips, particularly on active modes like walking and biking in the Canadian context. Results show that, compared to when the temperature is below 0 °C, respondents are 6% and 8% more likely to make a trip while the temperature is between 0 °C and 10 °C and higher than 10 °C, respectively. Respondents are about 4% more likely to choose an active mode when they have to cross only one or two major intersections compared to when they have to cross three or four major intersections. Respondents are about 21% more likely to choose an active mode when they have to take a road with a low speed limit and few vehicles compared to when they have to travel on roads with a low speed limit and many vehicles. Respondents are about 7% more likely to choose an active mode when they have to travel a path with ample availability of vegetation/trees or shelter compared to travelling a path with a limited availability of vegetation/trees or shelter. The effects of various sociodemographic and travel characteristics on mode choice are also discussed. We also analyzed the impacts of various categories such as individual and household sociodemographic, employment attributes, and trip characteristics on the choice of departure time. The results show that females are more likely than males to depart in afternoons and evenings, whereas males are more likely to depart in the mornings; that trips made by transit with a car and non-motorized vehicle (NMV) access are more likely to occur in the afternoon than trips made with any other travel mode; that respondents are less likely to depart in the afternoon than in the morning under snowy and rainy conditions; that respondents are more likely to depart in the afternoon when the temperature is below 0 °C than when the temperature is above 10 °C; and that respondents are more likely to depart in the afternoon and evening for trips related to grocery shopping, meals, and social/recreational events. Policy level implications of this study’s key findings are also discussed in detail.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".