Prediction of Recreational Travel Using Genetically Designed Regression and Time-Delay Neural Network Models
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
Selection of appropriate input variables is a crucial step in developing the statistical or neural network model for short-term traffic prediction. Recently, genetic algorithms have provided some success in input variable selection. Extensive experimentation with recreational traffic volume projections from Banff National Park in Alberta, Canada, is reported. Genetic algorithms (GAs) were used to select a set of historical traffic volumes that had higher correlation to the next hourly traffic volume. Universal models developed using GAs were accurate within 10%, on average. Separation of time series for individual hours revealed a linear trend in traffic volumes. Genetically designed regression submodels for individual hours had average prediction errors of less than 1% for the training sets. Even the 95th-percentile errors for the test sets were between 2% and 8%. Many highway agencies expect to deploy an advanced traveler information system (ATIS) for all highway categories. On the basis of such accurate predictions of traffic conditions from an ATIS, recreational drivers will be able to reschedule their travel time as well as routes. Such rescheduling will alleviate stress caused by traffic congestion during recreational travel.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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