Investigation of the Use of Smartphone Application for Trip Planning and Travel Outcome
Bibliographic record
Abstract
This paper aims to explore the use of smartphone applications for trip planning and travel outcome using data derived from a survey conducted in Halifax, Nova Scotia, in 2015. Through a comprehensive exploratory analysis, this study examines key trip planning decisions such as destination choice, departure time, mode choice, etc., and travel outcomes such as vehicle kilometers travelled, number of social gatherings attended, number of new places visited, and number of trips planned in groups. It also investigates the use of smartphone applications for specific travel needs such as reserving taxis, checking bus schedules, finding a location, etc. Several sets of factors such as trip makers’ characteristics, smartphone usage frequency, and travel characteristics are tested and interpreted. The results show that younger individuals’ travel behavior is mostly influenced by smartphone applications. The use of smartphone applications for trip planning and travel needs is increasing with the increase in years of smartphone use. Transit pass owners (67.87%) are the frequent users of smartphone applications for trip planning. Findings also suggest that transit and active transportation users more commonly use smartphone applications for deciding departure time and mode choice. Surprisingly, use of a smartphone does not have any substitution effect on travel outcome as reported by the respondents. In most cases, it has both neutral and complementary effect. For example, 49.9% reported ‘No Impact’ and 48.8% reported ‘Increase’ in number of new places visited. The study offers an in-depth understanding on how smartphone applications are affecting everyday activity planning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".