Investigating the Role of Built Environment and Lifestyle Choices in Active Travel for Weekly Home-Based Nonwork Trips
Bibliographic record
Abstract
This paper examines the influence of the built environment and individual lifestyle choices on weekly frequency of active transportation (AT) for home-based nonwork trips in the Halifax Regional Municipality, Nova Scotia, Canada. The growing trend of auto-oriented lifestyle choices and dependency not only intensifies environmental emissions and energy use but leads to decreased AT use for daily activities. Studies show that the built environment plays a significant role in active mode choice for nonwork trips, which are more flexible in time and location. However, research investigating the effect of lifestyle and attitudes combined with built-environment attributes for AT use has been limited, particularly for weeklong nonwork trips. Accordingly, this study investigated weekly active travel for nonwork trips on the basis of data from the Halifax Household Mobility and Travel Survey. Factor analysis captured the effects of lifestyle choices and attitudes, which were categorized by individuals' attitudes toward (a) travel modes and (b) land use and the environment. From loading the factors of the lifestyle choice variables, nine attitudinal variables were obtained. The study employed an ordered probit modeling framework to examine relative contributions of relevant socioeconomic characteristics, built-environment attributes, and attitudinal factors. Model results revealed that attitudes and factors of lifestyle choice played important roles in weekly AT frequency for nonwork trips. For example, individuals with prowalk–probike and protransit attitudes were likely to use AT more frequently for non-work-related trips than their counterparts. Although the impact of individuals' attitudes on AT trips was evident, this model also demonstrated that the built environment significantly influences the frequency of AT trips.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".