Lessons from a Large-Scale Experiment on the Use of Smartphone Apps to Collect Travel Diary Data: The “City Logger” for the Greater Golden Horseshoe Area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smartphones offer a potential alternative to collect high-quality information on the travel patterns of individuals without burdening the respondents with reporting every detail of their travel. Smartphone apps have recently become a common tool for travel survey data collection around the world, especially for multiday surveys. However, there still exists a lack of systematic assessment of issues related to smartphone app-based surveys, such as the impact of app design or the recruitment method on the collected data. Through a large-scale experiment (named the City Logger), this paper assesses the data produced by the City Logger app, to better understand recruitment avenues (targeted invitation versus crowdsourcing), and examine differences in respondents’ travel behavior recruited through crowdsourcing methods. The paper also examines how app design, and particularly the user input method for trip validation, influences participants’ responses. The results indicate that, while crowdsourcing recruitment is promising, it might not yet be the best way to capture a true representation of the population. For app design, a combination of real-time and travel diary approaches is recommended. An ideal app would prompt users real-time and create a travel diary, so users can validate, edit, or delete the recorded information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it