Comparison of neural classifiers and conventional approaches to mode choice analysis
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
This thesis provides a comparison of three modeling techniques which can be used for mode choice analysis. The techniques include the conventional logit, artificial neural networks (ANNs), and neurofuzzy models. The three modeling techniques were applied to mode choice data extracted from the 1999 24-hour trip diary survey of the Greater Vancouver Regional District. The travel mode of each individual was explained using explanatory variables acquired from three categories of the database: household database, personal database, and trip database. The results showed that, as modeling techniques, both ANNs and neurofuzzy models are highly adaptive and very efficient in dealing with problems involving complex interrelationships among many variables. The neurofuzzy technique combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. In addition; the neurofuzzy technique only selects the variables that significantly influence mode choice and display the stored knowledge in terms of fuzzy linguistic rules. This allows the modal decision making process to be examined and understood in great detail. The results of the comparison also indicated that neurofuzzy models produced the best results in terms of model accuracy. As well, it selected the least number of variables to achieve these results.
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 | 0.000 |
| Science and technology studies | 0.000 | 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