Augmenting Transit Trip Characterization and Travel Behavior Comprehension
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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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".