Travel Behavior Analysis for Activity-Based Travel Demand Modeling: A Case Study of the Tampa Bay Region
Bibliographic record
Abstract
Activity-based approach has been argued to be an advanced alternative to traditional four-step model, due to the higher fidelity and better policy sensitivity provided. This study aims to provide an initial analysis of travel diary data in the study area of Tampa Bay Region, Florida in a GIS environment. By visualizing the time-space paths of travelers and providing detailed statistics, this paper investigates the superiorities of using the detailed travel diary data for modeling the travel behaviors at both individual and household level. Individual's activity participations and durations are plotted using time-space path of each person in ArcScence. Detailed statistical comparisons of travel characteristics (including travel time, stops, distance, duration, and time-of-day) are also made among different employment groups. Household interaction is examined in a 3-D time-space environment with a comparison among different life-style household types. Descriptive statistics as well as the time geography analysis of travel behavior reported in this research are helpful for analyzing individual activity patterns and household interactions in a space-time context, and also provide supporting evidences of the superiorities of activity-based approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".