Identification of Representative Patterns of Time Use Activity Through Fuzzy <i>C</i> -Means Clustering
Bibliographic record
Abstract
Analysis of the time use activity patterns of urbanites will contribute greatly to the modeling of urban transportation demands by linking activity generation and activity scheduling modules in the overall activity-based modeling framework. This paper develops a framework for novel pattern recognition modeling to identify groups of individuals with homogeneous daily activity patterns. The framework consists of four modules: initialization of the total cluster number and cluster centroids, identification of individuals with homogeneous activity patterns and grouping of them into clusters, identification of sets of representative activity patterns, and exploration of interdependencies among the attributes in each identified cluster. Numerous new machine-learning techniques, such as the fuzzy C-means clustering algorithm and the classification and regression tree classifier, are employed in the process of pattern recognition. The 24-h activity patterns are split into 288 intervals of 5-min duration. Each interval includes information on activity types, duration, start time, location, and travel mode, if applicable. Aggregated statistical evaluation and Kolmogorov–Smirnov tests are performed to determine statistical significance of clustered data. Results show a heterogeneous diversity in eight identified clusters in relation to temporal distribution and significant differences in a variety of sociodemographic variables. The insights gained from this study include important information on activities—such as activity type, start time, duration, location, and travel distance—that are essential for the scheduling phase of the activity-based model. Finally, the results of this paper are expected to be implemented within the activity-based travel demand model for Halifax, Nova Scotia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".