Using Decision Tree Induction Systems for Modeling Space‐Time Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Discrete choice models are commonly used to predict individuals' activity and travel choices either separately or simultaneously in activity‐scheduling models. This paper investigates the possibilities of decision tree induction systems as an alternative approach. The ability of decision trees to represent heuristic decision rules is evaluated and a method of capturing interactions across decisions in a sequential decision model is outlined. Decision tree induction algorithms, such as C4.5, CART, and CHAID, are suited to derive the decision rules from empirical data. A case study to illustrate the approach considers decisions of individuals when they are faced with the choice to combine different out‐of‐home activities into a multipurpose, multistop trip or make a trip for each activity separately. Data from a large‐scale activity diary survey are used to induce the decision rules. Possible directions of future research are identified.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it