Social Context of Activity Scheduling
Bibliographic record
Abstract
Activity-based approaches to travel demand modeling are increasingly moving from theoretical to operational models. Agent-based microsimulation models are a promising approach as they explicitly conceive travel as an emergent phenomenon from people's activity characteristics and, more explicitly, from their activity-scheduling processes. Activity-scheduling processes are influenced by individuals’ characteristics as well as by the people with whom they interact. Thus, the activity-scheduling process has an intrinsic social context. With social activities used as a case study, the objective of this paper is to investigate empirically the relationship between social context (measured by with whom respondents interacted) and two key aspects of activity scheduling: start time and duration. Econometric models of the combined decisions of with whom to participate and when to start or how much time to spend are estimated to investigate the correlations between “with whom” and start time and duration decisions. Data collected by a 7-day activity diary survey were used for model development. Findings suggest that social context has a relevant role in activity-scheduling processes. For example, with whom people socialize influences social activity-scheduling processes more than do travel time or distances to social travel. In addition to theoretical understanding of the questions investigated here, the models serve as an empirical support for agent-based microsimulation models that could incorporate the role of social networks in activity-scheduling attributes.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".