Intrapersonal changes in social activity-travel patterns: linking time use and social network dynamics
Bibliographic record
Abstract
Motivation and objectives Recent interest on intrapersonal activity-travel dynamics has emphasized the need of understanding the link between long and short term behavioural processes in an integrated way, incorporating life events as well as daily decision making. Most of this research has focused on aggregated analysis of travel times and specific decisions, such as car ownership, with increasing focus on specific trip purposes. In this context, social activity-travel constitutes a particularly important subset, not only because it has an increasing high share on total trips, but also since it serves as a key instrument for people’s provision of social capital and quality of life. Yet, although recent studies have advanced on understanding and modeling the role of transport on social interactions, there is still the need of further evidence on the dynamic processes embedded in the context of social activities (Sharmeen et al., 2013; 2014a; 2014b). The objective of this paper is to study the interpersonal changes in social activity-travel behaviour by linking people’s daily activity-travel patterns with their social interaction context. We study these changes through data that explicitly collects information about people’s time use patterns and personal networks in two points in time, with five years of difference. Data and Methods For this, we study a sample of 105 individuals who were surveyed in 2008 and 2012 in the city of Concepcion, Chile. The survey gathered information about key socio-demographics and household contextual attributes, as well as mobility tools and use, activity-travel patterns, and personal networks. Mobility tools and use included car and information and communication technologies availability and use. Personal network information was gathered using a name generator technique, which explicitly elicits social contacts with specific criteria. Information about these people includes relationships with the respondent and between them, frequency of interaction (face-to-face and virtual), place of residence, and place of most frequent interaction. Activity-travel patterns consisted of people’s time use in one weekday and one weekend day, not only including the activity or trip temporal and spatial characteristics, but information about with whom it was performed and an explicit link with the respondent’s personal network. Structural equation models are used to account for the dynamics on activity-travel patterns and social contacts between the two years on three ways. First, the model captures changes between time uses on both years to capture dynamics on space, time, socializing, and trends on activity purposes and trips. Second, the model captures changes on the respondent’s personal networks in terms of social contacts that leave, join or maintain on the network between the two years, as well as general trends in terms of the network’s structure and composition. Third, the model captures the inter-relationships between time use and personal networks, on each year as well as the changes in the five year period. The study adds to the state of the art on travel behaviour research on two aspects. First, we add more methodological experience on the opportunities and scope of using explicit personal network data to study intrapersonal activity-travel dynamics. Second, we add empirical evidence to the role of key aspects that define the link between short and long term behavioural processes, such as key life events, lifecycle, and the role of socio-demographics. References Sharmeen, F., T. A. Arentze, and H. J. P. Timmermans. A multilevel path analysis of 30 social network dynamics and the mutual interdependencies between face-to-face and ICT modes of social interaction in the context of life-cycle events.In Travel Behaviour Research: Current Foundations, Future Prospects , Lulu Publishers, Toronto, 2013, pp. 411-432. Sharmeen, F. A., T.Timmermans, H. An analysis of the dynamics of activity and travel 27 needs in response to social network evolution and life-cycle events: A structural equation model. Transportation Research Part A: Policy and Practice, Vol. 59, 2014(a), pp. 159-171. Sharmeen, F., T. Arentze, and H. Timmermans. An analysis of the dynamics of 35 activity and travel needs in response to social network evolution and life-cycle events: A 36 structural equation model. Transportation Research Part A: Policy and Practice, Vol. 59, 2014(b), pp. 159-171.
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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 | 0.004 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".