Comparison of Trip Generation Results from Activity-Based and Traditional Four-Step Travel Demand Modeling: A Case Study of Tampa, Florida
Bibliographic record
Abstract
There are two main modeling approaches in travel demand forecasting today: one is the traditional four-step travel demand model (FSM) that is being used by the majority of transportation planning agencies, and the other is activity-based model, which simulates individual and household activities at much more detailed levels. The activity-based approach has been viewed as a more advanced method than the traditional four-step model. This paper reports the partial results - trip generations only from a larger research project investigating the differences between the two modeling approaches. In order to do so, a micro-simulation model is developed and used to simulate 24-hour individual daily travel behaviors of the entire population in the Citrus County from Florida, based on survey data that are obtained from Tampa Bay Regional Transportation travel diary records. Corresponding to each step in the traditional four-step travel demand model, trip generation rates, trip distribution, mode choice percentages, and trip assignment results are either directly calculated from the observed or simulated individual daily travel records and are compared with the corresponding results from the Tampa four-step travel demand model (developed elsewhere). Study results show salient differences in modeling performance and accuracy in each of four steps above between four-step and activity-based travel demand approaches. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.000 | 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) | 0.001 | 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 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".