Exploring Travel Behavior of Households with Pre-School Aged Children
Bibliographic record
Abstract
Child-care centers are major trip generators for households with pre-school aged children (< 5 years). This paper presents a framework to analyze the factors affecting daily travel choices and challenges of child-care going households. Through rigorous literature review, this study first identifies important variables affecting mode choice, child-care center choice, transit use, and intention to drive. Next, it develops and distributes an online survey questionnaire to the registered child-care centers in Halifax Regional Municipality, Canada, to get data on individual households’ travel choices, challenges, factors affecting those choices, and importance level of those factors, as well as socio-demographic information. The study collects data from over 200 households, which is then analyzed using structural equation modeling to examine the effects of the identified factors such as safety, cost, distance, location, affordability, and convenience on day-to-day child-care trip decisions. The findings suggest that most of the families (73%) rely on private vehicles to make child-care trips as a result of family time, travel freedom, and parking availability. Other variables that motivate them to drive are travel safety, convenience, and opportunity to do trip chaining. Results show that 57% of the households group their child-care trip with a work trip. Distance to nearest bus stop, carrying child belongings, and length of trip to child-care are considered major barriers for transit use. The results of this study are expected to assist policy makers to understand travel patterns and barriers of households with pre-school aged children and help in designing communities to promote sustainable 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".