Augmenting Transit Trip Characterization and Travel Behavior Comprehension
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
Trips need to be described and have always been characterized by various levels of abstraction. It varies from a simple label such as home-based work to complete itinerary with sociodemographic characteristics of the trip maker and household. The rationale behind such classifications is that planners and modelers recognize that the demand of transportation is highly differentiated. It is hoped that additional attributes would provide a more complete portrait of the demand and an improved understanding of the underlying travel behavior. Passive data collection technologies bring an extra dimension to travel data acquisition. Multiday data, which are difficult to collect, become accessible. In public transit, a smart card automatic fare collection system with automatic vehicle location capability provides high-resolution longitudinal data on travel pattern but also suffers from the inherent limitations of passive methods. This paper proposes a methodology to enhance transit trip characterization by adding a multiday dimension to a month of smart card transactions. On the basis of an individual, anchor points—precise to an exact address—are detected. Boarding and alighting locations are described with respect to those anchors. The enhancement allows in-depth travel behavior analysis on a subgroup sharing a common anchor or an individual. The paper demonstrates the use of spatial statistics, spatial analyses with geographic information system, visualizations, and data mining to describe activity space and locations and departure time dynamics, and to derive monthly trip table, activity schedule, and behavioral rules for cardholders. The results offer promising insights to transit planning and the understanding of travel behavior.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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