Impact of Life Events on Incremental Travel Behavior Change
Bibliographic record
Abstract
Travel demand models regenerate the travel behavior of persons or households from scratch at every model run. However, the literature suggests that travel behavior remains relatively stable over time. Change in travel behavior is triggered by life events such as change in employment, household relocation, or birth of a child. The inability of existing travel demand models to represent habitual travel behavior and change in travel behavior of a person/household becuase of life events tends to exaggerate policy sensitivity and result in longer model run times to recreate travel behavior for every agent. In this study, we examined the travel behavior of persons between two consecutive years using a mobility panel survey from Germany. The travel behavior of persons with and without a life event is compared econometrically. Here, the travel behavior is measured as the number of weekly trips by activity type and mode and the impacts of six types of life events are studied. The results show that life events affect travel behavior, but the degree of impact varies by the type of life event, the trip purpose, and the mode. In some cases the impact is found to be negligible, but for many other cases the impact is profound. Moreover, general trends (not affected by life event) in travel behavior are also found. It is concluded that such dynamics in travel behavior should be represented by travel demand models for more sensible policy testing and computationally efficient travel demand models.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".