Developing Skeletal Activity Scheduler using Machine Learning
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
Understanding human mobility patterns is crucial for sustainable urban planning. This study presents a novel approach for predicting daily activity sequences using machine learning techniques, specifically Long Short-Term Memory (LSTM) networks and Explainable Boosting Machines (EBM). Utilizing data from the 2022 Halifax Travel Activity (HaliTRAC) Survey, we train these models to predict sequences of activities based on individual and household characteristics, aiming to balance predictive performance with interpretability. The LSTM model effectively captures complex temporal dependencies, while EBM provides clear insights into the significance of individual features, addressing the "black box" nature of Machine Learning models. By simplifying activity sequences into five primary activity types, the refined LSTM and EBM models achieve accuracies of 70.25% and 73.73%, respectively. Key findings highlight employment status, age, and education level as major determinants of activity patterns, with household characteristics like size playing a secondary role. This research demonstrates the potential of utilizing advanced machine learning techniques in mobility analysis, offering both accurate predictions and actionable insights. The proposed framework provides a foundation for developing transparent and reliable tools to inform transportation policies and urban development strategies.
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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 | 0.001 |
| 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