Life-Oriented Approach of Modeling Commute Mode Loyalty and Transition Behavior
Bibliographic record
Abstract
This study developed a dynamic model for individuals’ commute mode choice over their lifetime by using retrospective survey data. The study conceptualized that individuals reassessed their choice of commute mode when they relocated to a new residential location. Following the re-appraisal, people either continued using the same mode, which was considered mode loyalty, or made a transition to a new mode, which was considered mode transition in this study. The study developed a panel-based random-parameters logit model. One key feature of this study is a life-oriented approach to accommodate the effects of life-cycle events, longer-term changes, life-oriented sociodemographic transitions, and accessibility transitions. The model results suggest that the high-income group tends to be car loyal. No car ownership over the lifetime and the addition of a job increase the probability of transit loyalty. Individuals with no children in the household and residing in an area with high walk and bike usage have a higher probability to be loyal to active transportation. A decrease in household income and tenure transition from owned to rental are likely to trigger a transition from car to transit. However, the presence of children and the addition of a car increase the transition propensity from transit to car. The model results suggest that the use of life-oriented characteristics to explain longer-term commute mode loyalty and transition behavior provides important behavioral insights into the dynamics of individuals’ travel behavior over their lifetime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".