MOVING BEYOND OBSERVED OUTCOMES: INTEGRATING GLOBAL POSITIONING SYSTEMS AND INTERACTIVE COMPUTER-BASED TRAVEL BEHAVIOR SURVEYS
Bibliographic record
Abstract
This paper focuses on the use of the Global Positioning System (GPS) to enhance and extend travel behavior survey methods. The paper first describes the testing of a passive vehicle-based GPS tracking system in Quebec City, then describes the development of algorithms with a geographic information system (GIS) that can be used to automatically match the GPS data to road segments along a network, and identify stops along the way. While such processing results in a very detailed depiction of travel, key pieces of information are still needed to complete the pattern of travel--including in the least, trip purpose, multi-stop information, and short undetected stops. Perhaps more seriously, such data are limited to the observed patterns, which does little to explain the underlying behavioral processes that led to the observed patterns. While many researchers agree that investigation of these processes is crucial to an improved understanding of travel behavior, existing GPS-related travel surveys are limited to the replication of observed travel patterns in parallel to traditional trip/activity diary surveys, albeit with a higher level of detail. This paper attempts to explore how GPS traced routes and stops could be used as a memory jogger for more in-depth explorations of travel behavior in a home-based survey approach. These include exploration of more detailed spatial-temporal patterns and the decision processes that underlie route and activity-travel scheduling decisions. This paper culminates in the description of a comprehensive approach that combines GPS and GIS technologies with a recently developed computerized activity scheduling survey that has the potential to simultaneously observe detailed spatial-temporal activity-travel patterns and underlying decision processes of individuals within a household over long periods of time, while at the same time minimizing respondent burden.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".